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It remains unknown whether myonuclei remain elevated post anabolic-androgenic steroid (AAS) usage in humans. Limited data exist on AAS-induced changes in gene expression.Cross-sectional/longitudinal.University.Fifty-six men aged 20 to 42 years.Non-resistance-trained (C) or resistance-trained (RT), RT currently using AAS (RT-AS), of which if AAS usage ceased for ≥18 weeks resampled as Returning Participants (RP) or RT previously using AAS (PREV).Myonuclei per fiber and cross-sectional area (CSA) of trapezius muscle fibers.There were no significant differences between C (n = 5), RT (n = 15), RT-AS (n = 17), and PREV (n = 6) for myonuclei per fiber. Three of 5 returning participants (RP1-3) were biopsied twice. Before visit 1, RP1 ceased AAS usage 34 weeks before, RP2 and RP3 ceased AAS usage ≤2 weeks before, and all had 28 weeks between visits. Fiber CSA decreased for RP1 and RP2 between visits (7566 vs 6629 μm2; 7854 vs 5677 μm2) while myonuclei per fiber remained similar (3.5 vs 3.4; 2.5 vs 2.6). Respectively, these values increased for RP3 between visits (7167 vs 7889 μm2; 2.6 vs 3.3).This cohort of past AAS users did not have elevated myonuclei per fiber values, unlike previous research, but reported AAS usage was much lower. Training and AAS usage history also varied widely among participants. Comparable myonuclei per fiber numbers despite decrements in fiber CSA postexposure adheres with the muscle memory mechanism, but there is variation in usage relative to sampling date and low numbers of returning participants.

The Global Alliance for Genomics and Health (GA4GH) aims to accelerate biomedical advances by enabling the responsible sharing of clinical and genomic data through both harmonized data aggregation and federated approaches. The decreasing cost of genomic sequencing (along with other genome-wide molecular assays) and increasing evidence of its clinical utility will soon drive the generation of sequence data from tens of millions of humans, with increasing levels of diversity. In this perspective, we present the GA4GH strategies for addressing the major challenges of this data revolution. We describe the GA4GH organization, which is fueled by the development efforts of eight Work Streams and informed by the needs of 24 Driver Projects and other key stakeholders. We present the GA4GH suite of secure, interoperable technical standards and policy frameworks and review the current status of standards, their relevance to key domains of research and clinical care, and future plans of GA4GH. Broad international participation in building, adopting, and deploying GA4GH standards and frameworks will catalyze an unprecedented effort in data sharing that will be critical to advancing genomic medicine and ensuring that all populations can access its benefits., B.P.C. acknowledges funding from Abigail Wexner Research Institute at Nationwide Children’s Hospital; T.H. Nyrönen acknowledges funding from Academy of Finland grant #31996; A.M.-J., K.N., T.F.B., O.M.H., and Z.S. acknowledge funding from Australian Medical Research Future Fund; M.S. acknowledges funding from Biobank Japan; D. Bujold and S.J.M.J. acknowledge funding from Canada Foundation for Innovation; L.J.D. acknowledges funding from Canada Foundation for Innovation Cyber Infrastructure grant #34860; D. Bujold and G.B. acknowledge funding from CANARIE; L.J.D. acknowledges funding from CANARIE Research Data Management contract #RDM-090 (CHORD) and #RDM2-053 (ClinDIG); K.K.-L. acknowledges funding from CanSHARE; T.L.T. acknowledges funding from Chan Zuckerberg Initiative; T. Burdett acknowledges funding from Chan Zuckerberg Initiative grant #2017-171671; D. Bujold, G.B., and L.D.S. acknowledge funding from CIHR; L.J.D. acknowledges funding from CIHR grant #404896; M.J.S.B. acknowledges funding from CIHR grant #SBD-163124; M. Courtot and M. Linden acknowledge funding from CINECA project EU Horizon 2020 grant #825775; D. Bujold and G.B. acknowledge funding from Compute Canada; F.M.-G. acknowledges funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – NFDI 1/1 “GHGA – German Human Genome-Phenome Archive; R.M.H.-S. acknowledges funding from Duke-Margolis Center for Health Policy; S.B. and A.J.B. acknowledge funding from EJP-RD EU Horizon 2020 grant #825575; A. Niewielska, A.K., D.S., G.I.S., J.A.T., J.R., M.A.K., M. Baudis, M. Linden, S.B., S.S., T.H. Nyrönen, and T.M.K. acknowledge funding from ELIXIR; A. Niewielska acknowledges funding from EOSC-Life EU Horizon 2020 grant #824087; J.-P.H. acknowledges funding from ETH Domain Strategic Focal Area “Personalized Health and Related Technologies (PHRT)” grant #2017-201; F.M.-G. acknowledges funding from EUCANCan EU Horizon 2020 grant #825835; B.M.K., D. Bujold, G.B., L.D.S., M.J.S.B., N.S., S.E.W., and Y.J. acknowledge funding from Genome Canada; B.M.K., M.J.S.B., S.E.W., and Y.J. acknowledge funding from Genome Quebec; F.M.-G. acknowledges funding from German Human Genome-Phenome Archive; C. Voisin acknowledges funding from Google; A.J.B. acknowledges funding from Health Data Research UK Substantive Site Award; D.H. acknowledges funding from Howard Hughes Medical Institute; S.B. acknowledges funding from Instituto de Salud Carlos III; S.-S.K. and K.T. acknowledge funding from Japan Agency for Medical Research and Development (AMED); S. Ogishima acknowledges funding from Japan Agency for Medical Research and Development (AMED) grant #20kk0205014h0005; C.Y. and K. Kosaki acknowledge funding from Japan Agency for Medical Research and Development (AMED) grant #JP18kk0205012; GEM Japan acknowledges funding from Japan Agency for Medical Research and Development (AMED) grants #19kk0205014h0004, #20kk0205014h0005, #20kk0205013h0005, #20kk0205012h0005, #20km0405401h0003, and #19km0405001h0104; J.R. acknowledges funding from La Caixa Foundation under project #LCF/PR/GN13/50260009; R.R.F. acknowledges funding from Mayo Clinic Center for Individualized Medicine; Y.J. and S.E.W. acknowledge funding from Ministère de l’Économie et de l’Innovation du Québec for the Can-SHARE Connect Project; S.E.W. and S.O.M.D. acknowledge funding from Ministère de l’Économie et de l’Innovation du Québec for the Can-SHARE grant #141210; M.A.H., M.C.M.-T., J.O.J., H.E.P., and P.N.R. acknowledge funding from Monarch Initiative grant #R24OD011883 and Phenomics First NHGRI grant #1RM1HG010860; A.L.M. and E.B. acknowledge funding from MRC grant #MC_PC_19024; P.T. acknowledges funding from National University of Singapore and Agency for Science, Technology and Research; J.M.C. acknowledges funding from NHGRI; A.H.W. acknowledges funding from NHGRI awards K99HG010157, R00HG010157, and R35HG011949; A.M.-J., K.N., D.P.H., O.M.H., T.F.B., and Z.S. acknowledge funding from NHMRC grants #GNT1113531 and #GNT2000001; D.L.C. acknowledges funding from NHMRC Ideas grant #1188098; A.B.S. acknowledges funding from NHMRC Investigator Fellowship grant #APP177524; J.M.C. and L.D.S. acknowledge funding from NIH; A.A.P. acknowledges funding from NIH Anvil; A.V.S. acknowledges funding from NIH contract #HHSN268201800002I (TOPMed Informatics Research Center); S.U. acknowledges funding from NIH ENCODE grant #UM1HG009443; M.C.M.-T. and M.A.H. acknowledge funding from NIH grant #1U13CA221044; R.J.C. acknowledges funding from NIH grants #1U24HG010262 and #1U2COD023196; M.G. acknowledges funding from NIH grant #R00HG007940; J.B.A., S.L., P.G., E.B., H.L.R., and L.S. acknowledge funding from NIH grant #U24HG011025; K.P.E. acknowledges funding from NIH grant #U2C-RM-160010; J.A.E. acknowledges funding from NIH NCATS grant #U24TR002306; M.M. acknowledges funding from NIH NCI contract #HHSN261201400008c and ID/IQ Agreement #17X146 under contract #HHSN2612015000031 and #75N91019D00024; R.M.C.-D. acknowledges funding from NIH NCI grant #R01CA237118; M. Cline acknowledges funding from NIH NCI grant #U01CA242954; K.P.E. acknowledges funding from NIH NCI ITCR grant #1U24CA231877-01; O.L.G. acknowledges funding from NIH NCI ITCR grant #U24CA237719; R.L.G. acknowledges funding from NIH NCI task order #17X147F10 under contract #HHSN261200800001E; A.F.R. acknowledges funding from NIH NHGRI grant #RM1HG010461; N.M. and L.J.Z. acknowledge funding from NIH NHGRI grant #U24HG006941; R.R.F., T.H. Nelson, L.J.B., and H.L.R. acknowledge funding from NIH NHGRI grant #U41HG006834; B.J.W. acknowledges funding from NIH NHGRI grant #UM1HG009443A; M. Cline acknowledges funding from NIH NHLBI BioData Catalyst Fellowship grant #5118777; M.M. acknowledges funding from NIH NHLBI BioData Catalyst Program grant #1OT3HL142478-01; N.C.S. acknowledges funding from NIH NIGMS grant #R35-GM128636; M.C.M.-T., M.A.H., P.N.R., and R.R.F. acknowledge funding from NIH NLM contract #75N97019P00280; E.B. and A.L.M. acknowledge funding from NIHR; R.G. acknowledges funding from Project Ris3CAT VEIS; S.B. acknowledges funding from RD-Connect, Seventh Framework Program grant #305444; J.K. acknowledges funding from Robertson Foundation; S.B. and A.J.B. acknowledge funding from Solve-RD, EU Horizon 2020 grant #779257; T.S. and S. Oesterle acknowledge funding from Swiss Institute of Bioinformatics (SIB) and Swiss Personalized Health Network (SPHN), supported by the Swiss State Secretariat for Education, Research and Innovation SERI; S.J.M.J. acknowledges funding from Terry Fox Research Institute; A.E.H., M.P.B., M. Cupak, M.F., and J.F. acknowledge funding from the Digital Technology Supercluster; D.F.V. acknowledges funding from the Australian Medical Research Future Fund, as part of the Genomics Health Futures Mission grant #76749; M. Baudis acknowledges funding from the BioMedIT Network project of Swiss Institute of Bioinformatics (SIB) and Swiss Personalized Health Network (SPHN); B.M.K. acknowledges funding from the Canada Research Chair in Law and Medicine and CIHR grant #SBD-163124; D.S., G.I.S., M.A.K., S.B., S.S., and T.H. Nyrönen acknowledge funding from the EU Horizon 2020 Beyond 1 Million Genomes (B1MG) Project grant #951724; P.F., A.D.Y., F.C., H.S., I.U.L., D. Gupta, M. Courtot, S.E.H., T. Burdett, T.M.K., and S.F. acknowledge funding from the European Molecular Biology Laboratory; Y.J. and S.E.W. acknowledge funding from the Government of Canada; P.G. acknowledges funding from the Government of Canada through Genome Canada and the Ontario Genomics Institute (OGI-206); J.Z. acknowledges funding from the Government of Ontario; C.K.Y. acknowledges funding from the Government of Ontario, Canada Foundation for Innovation; C. Viner and M.M.H. acknowledge funding from the Natural Sciences and Engineering Research Council of Canada (grant #RGPIN-2015-03948 to M.M.H. and Alexander Graham Bell Canada Graduate Scholarship to C.V.); K.K.-L. acknowledges funding from the Program for Integrated Database of Clinical and Genomic Information; J.K. acknowledges funding from the Robertson Foundation; D.F.V. acknowledges funding from the Victorian State Government through the Operational Infrastructure Support (OIS) Program; A.M.L., R.N., and H.V.F. acknowledge funding from Wellcome (collaborative award); F.C., H.S., P.F., and S.E.H. acknowledge funding from Wellcome Trust grant #108749/Z/15/Z; A.D.Y., H.S., I.U.L., M. Courtot, H.E.P., P.F., and T.M.K. acknowledge funding from Wellcome Trust grant #201535/Z/16/Z; A.M., J.K.B., R.J.M., R.M.D., and T.M.K. acknowledge funding from Wellcome Trust grant #206194; E.B., P.F., P.G., and S.F. acknowledge funding from Wellcome Trust grant #220544/Z/20/Z; A. Hamosh acknowledges funding from NIH NHGRI grant U41HG006627 and U54HG006542; J.S.H. acknowledges funding from National Taiwan University #91F701-45C and #109T098-02; the work of K.W.R. was supported by the Intramural Research Program of the National Library of Medicine, NIH. For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. H.V.F. acknowledges funding from Wellcome Grant 200990/A/16/Z ‘Designing, developing and delivering integrated foundations for genomic medicine'.

Neurofibromatosis type 2 (NF2) is a rare autosomal dominant disorder (incidence 1:33 000-40 000) characterized by formation of central nervous system tumors, due to mutation in the NF2 gene on chromosome 22q12. Vestibular schwannomas are the hallmark lesion, affecting 95% of individuals and typically occur bilaterally. Schwannomas commonly occur on other nerves intracranially and in the spinal compartment, along with meningiomas, ependymomas, and gliomas. Although histologically benign, tumors are associated with significant morbidity due to multiple problems including hearing and vision loss, gait abnormalities, paralysis, pain, and seizures. Risk of early mortality from brainstem compression and other complications is significant. Severity of disease is higher when NF2 presents during childhood. Children have a more variable presentation, which can be associated with significant delays in recognition of the condition. Careful examination of the skin and eyes can identify important clinical signs of NF2 during childhood, allowing timely initiation of disease-specific surveillance and treatment. Monitoring for complications comprises clinical evaluation, along with functional testing including audiology and serial neuroimaging, which together inform decisions regarding treatment. Evidence for disease-specific medical treatment options is increasing, nevertheless most patients will benefit from multimodal treatment including surgery during their lifetime. Patient enrolment in international natural history and treatment trials offers the best opportunity to accelerate our understanding of the complications and optimal treatment of NF2, with a view to improving outcomes for all affected individuals.

To evaluate the diagnostic outcomes in a large cohort of congenital muscular dystrophy (CMD) patients using traditional and next generation sequencing (NGS) technologies.A total of 123 CMD patients were investigated using the traditional approaches of histology, immunohistochemical analysis of muscle biopsy, and candidate gene sequencing. Undiagnosed patients available for further testing were investigated using NGS.Muscle biopsy and immunohistochemical analysis found deficiencies of laminin α2, α-dystroglycan, or collagen VI in 50% of patients. Candidate gene sequencing and chromosomal microarray established a genetic diagnosis in 32% (39 of 123). Of 85 patients presenting in the past 20 years, 28 of 51 who lacked a confirmed genetic diagnosis (55%) consented to NGS studies, leading to confirmed diagnoses in a further 11 patients. Using the combination of approaches, a confirmed genetic diagnosis was achieved in 51% (43 of 85). The diagnoses within the cohort were heterogeneous. Forty-five of 59 probands with confirmed or probable diagnoses had variants in genes known to cause CMD (76%), and 11 of 59 (19%) had variants in genes associated with congenital myopathies, reflecting overlapping features of these conditions. One patient had a congenital myasthenic syndrome, and 2 had microdeletions. Within the cohort, 5 patients had variants in novel (PIGY and GMPPB) or recently published genes (GFPT1 and MICU1), and 7 had variants in TTN or RYR1, large genes that are technically difficult to Sanger sequence.These data support NGS as a first-line tool for genetic evaluation of patients with a clinical phenotype suggestive of CMD, with muscle biopsy reserved as a second-tier investigation. Ann Neurol 2016;80:101-111.

Large community initiatives are a growing phenomenon both in the U.S. and worldwide. These initiatives address a wide variety of issues, including early childhood development, by integrating institutions such as schools, health agencies, and faith-based institutions that focus on separate but related aspects of community concern. A major challenge facing these initiatives is the competing demands of developing organizational capacity to promote effective programming while simultaneously delivering demonstrable results and accountability. Empowerment evaluation (Fetterman, Kaftarian, & Wandersman, 1996) is an approach to evaluation and organizational capacity building that equips participants at all levels of an organization to pursue programming quality and results. This article describes and presents lessons learned from the development and implementation of a system of tools and processes, grounded in the principles of empowerment evaluation, designed to promote quality in the planning, impleme...

The Global Alliance for Genomics and Health (GA4GH) was officially launched in March 2014. The aims of the Global Alliance are to accelerate the world-wide effort to responsibly aggregate, analyse and share large amounts of genomic and clinical information to advance the understanding, diagnosis, and treatment for cancer, inherited diseases, infectious diseases, and drug responses. The alliance consists of over 200 of the world's leading biomedical research institutions, healthcare providers, information technology and life science companies, funders of research, and disease and patient advocacy organizations. As part of four Working Groups (Clinical, Regulatory and Ethics, Data, and Security), Alliance Members have already developed products such as: • A Framework for Responsible Sharing of Genomic and Health-Related Data , containing foundational governance principles and guidelines for research. This Framework is guided by the Universal Declaration of Human Rights and the right of scientific freedom of all citizens to share in the benefits of scientific progress and for scientists to be recognised for their contributions. • A GA4GH Genomics API (published at www.ga4gh.org ) that allows researchers to exchange genomic information, including sequence reads and reference variation, across organisations and platforms. The Genomics API comes with a compliance test and reference implementation, to facilitate adoption and ensure interoperability. The Global Alliance is also supporting key data sharing projects being undertaken by Alliance Members. These projects will demonstrate value and promote real-world learning: • An international Matchmaker Exchange, that will facilitate researchers and clinicians finding other patients with similar rare phenotypic and genotypic profiles through a connected system of 'Matchmakers'. • The Global BRCA Challenge is being driven by leaders in the field internationally with the aim of sharing and pooling genotype and phenotype data. This initiative has the potential to dramatically increase our ability to estimate BRCA1 and BRCA2 -related cancer risks.

The human sarcomeric alpha-actinins (ACTN2 and ACTN3) are major structural components of the Z line in skeletal muscle; they play a role in the maintenance of sarcomeric integrity and also interact with a wide variety of structural, signaling and metabolic proteins. ACTN2 is expressed in all muscle fibers, and expression of ACTN3 is restricted to the type 2 (fast glycolytic) fibers that are responsible for forceful contraction at high velocity. There is a common stop codon polymorphism R577X in the ACTN3 gene. Homozygosity for the R577X null-allele results in the absence of alpha-actinin-3 in fast muscle fibers with frequencies that vary from1% in Africans to approximately 18% in Caucasians. A number of association studies have demonstrated that the ACTN3 R577X genotype influences athletic performance in Caucasians; the frequency of the XX genotype is significantly lower than controls in sprint athletes, and it appears that alpha-actinin-3 deficiency is detrimental to sprint performance. In the general population, the ACTN3 genotype contributes to the normal variations in muscle strength and sprinting speed. In an Actn3 knockout mouse model, alpha-actinin-3 deficiency is associated with a shift in the characteristics of fast, glycolytic 2B muscle fibers towards a slow phenotype, with decreased muscle mass and fiber diameter, slower contractile properties, increased fatigue resistance, and an increase in oxidative enzyme activity. The shift towards a more efficient oxidative metabolism may underlie the selective advantage of the X-allele during evolution. In turn, the shift towards a 'slow' muscle phenotype in fast muscle fibers likely explains why loss of alpha-actinin-3 is detrimental to sprint performance.

Mutations in ACTA1 have been associated with a variety of changes in muscle histology that likely result from fundamental differences in the way that ACTA1 mutations disrupt muscle function. Recently, we reported three patients with congenital fiber type disproportion (CFTD) caused by novel heterozygous missense mutations in ACTA1 (D292V, L221P, P332S) with marked type 1 fiber hypotrophy as the only pathological finding on muscle biopsy. We have investigated the basis for the histological differences between these CFTD patients and patients with ACTA1 nemaline myopathy (NM).Mass spectrometry and two-dimensional gel electrophoresis demonstrate that mutant actin accounts for 25 and 50% of alpha-skeletal actin in the skeletal muscle of patients with the P332S and D292V mutations, respectively, consistent with a dominant-negative disease mechanism. In vitro motility studies indicate that abnormal interactions between actin and tropomyosin are the likely principal cause of muscle weakness for D292V, with tropomyosin stabilized in the "switched off" position. Both the D292V and P322S CFTD mutations are associated with normal sarcomeric structure on electron microscopy, which is atypical for severe NM. In contrast, we found no clear difference between ACTA1 mutations associated with NM and CFTD in tendency to polymerize or aggregate in C2C12 expression models.These data suggest that ACTA1 CFTD mutations cause weakness by disrupting sarcomere function rather than structure. We raise the possibility that the presence or absence of structural disorganization when mutant actin incorporates into sarcomeres may be an important determinant of whether the histological patterns of CFTD or NM develop in ACTA1 myopathy.

Cognitive deficits and academic learning difficulties are the most common neurologic "complication" of neurofibromatosis 1 in childhood and can be responsible for significant lifetime morbidity. There is a slight increase in the frequency of mental retardation (Wechsler Full-Scale IQ70) in children with neurofibromatosis 1, but the mean Full-Scale IQ for the patient group is within 1 SD of the population mean. Academic difficulties are common, as are specific deficits in visuospatial ability, executive function, expressive and receptive language, and attentional skills. Behavioral and psychosocial problems have a major impact on quality of life, although there are few objective studies in this area Current research is focusing on the pathogenesis of the disorder. Clinical studies have identified possible radiologic and pathologic markers for cognitive deficits in neurofibromatosis 1, which can now be explored in animal models.

Muscular dystrophy includes many genetically distinct disorders. The list of causative genes for muscular dystrophy has been expanding rapidly, including those for congenital muscular dystrophies.We review the newly identified causative genes and suggested molecular mechanisms, focusing on glycosylation abnormality of alpha-dystroglycan, collagen VI deficiency, four allelic diseases of caveolin-3 gene, and titin gene mutations.Several possible mechanisms causing muscular dystrophy were discussed. Defects in extracellular molecules have more significant effects resulting mainly in congenital muscular dystrophy, while intracellular molecular defects show milder effect on the phenotype. These hypotheses may provide a new paradigm in understanding the pathomechanism of muscular dystrophies.

OBJECTIVE—To analyse the distributions of and generate growth charts for stature and occipitofrontal circumference (OFC) in neurofibromatosis 1 (NF1) patients. DESIGN—Cross sectional database survey. SETTING—The National Neurofibromatosis Foundation International Database (NFDB) includes clinical information on NF1 patients from 14 participating centres in North America. SUBJECTS—A total of 569 white, North American, NF1 patients, 55% female and 45% male. MAIN OUTCOME MEASURES—Stature and OFC measurements of NF1 patients were compared to age and sex matched population norms using z score standardisation and centile curves. RESULTS—The distributions of stature and OFC are shifted and unimodal among NF1 patients; 13% of patients have short stature (⩾2 standard deviations below the population mean) and 24% have macrocephaly (OFC ⩾2 standard deviations above the population mean). CONCLUSIONS—Alterations of stature and OFC are not limited to NF1 patients with frank short stature or macrocephaly. Keywords: neurofibromatosis 1; stature; occipitofrontal circumference; macrocephaly

Spinal muscular atrophy is a common cause of disability in childhood and is characterized by weakness and wasting of voluntary muscle. It is frequently fatal. The gene for this disorder has been identified as the SMN gene and is part of a highly complex duplicated region of chromosome 5 that is subject to a high rate of gene deletion and gene conversion. The severity of muscle weakness correlates with the amount of full-length SMN protein produced. Molecular genetic studies support a model in which patients are compound heterozygotes of deleted and converted alleles that predicts a progressively decreasing amount of protein product with severity of muscle weakness. The function of SMN is beginning to be understood and it appears to be involved in ribonucleoprotein biogenesis and thus indirectly in post-transcriptional processing of mRNA. There are theoretical grounds for motor neurons having a cell-specific vulnerability to disturbances of mRNA processing and transport and these are briefly reviewed.

The National Neurofibromatosis Foundation International Database is a system for collecting comprehensive information on the clinical manifestations and natural history of neurofibromatosis. Data are entered into personal computers at participating centres and are pooled at the Central Database and coordinating centre in Vancouver, Canada. The system includes special mechanisms to assure consistency among centres and to maintain patient confidentiality. The database is designed to foster collaborative clinical and molecular genetic research in many aspects of neurofibromatosis. © 1993 Wiley-Liss, Inc.

The aim of this study was to test the hypothesis that thalidomide acts upon the embryonic peripheral nervous system rather than upon mesenchyme. Pregnant rabbits were given oral thalidomide (150 mg/kg/day) on Days 7-11 of gestation. Fetuses were removed at laparotomy, under anaesthesia, on Day 29 of gestation. Seven fetuses with partial or total absence of the tibia, five treated fetuses without deformities, and four untreated controls were photographed, radiographed, killed and fixed for histological examination. Sciatic nerves were dissected and transverse sections were taken from an identical site. Total fascicular area, myelinated fibre number, fibre density and diameter distribution were obtained. There was a significant reduction in total fascicular area, and in the number of large diameter fibres in all treated animals. There was a significant depletion of total fibre numbers in deformed fetuses compared with controls. These findings are similar to the quantitative changes described in human adult subjects with thalidomide polyneuropathy, and are consistent with primary axonal degeneration in both instances. It is concluded that thalidomide acts upon embryonic nerves rather than on mesenchyme, and that dysmelic deformities of the limbs are secondary to toxic embryonic neuropathy. It is suggested that skeletal defects result when irreversible damage to the nerves reduces the transverse fascicular area below a critical minimum threshold.