Supplementary data are available at Bioinformatics online.

Phenylketonuria (PKU) is a common genetic metabolic disorder that affects the infant's nerve development and manifests as abnormal behavior and developmental delay as the child grows. Currently, a triple-quadrupole mass spectrometer (TQ-MS) is a common high-accuracy clinical PKU screening method. However, there is high false-positive rate associated with this modality, and its reduction can provide a diagnostic and economic benefit to both pediatric patients and health providers. Machine learning methods have the advantage of utilizing high-dimensional and complex features, which can be obtained from the patient's metabolic patterns and interrogated for clinically relevant knowledge. In this study, using TQ-MS screening data of more than 600,000 patients collected at the Newborn Screening Center of Shanghai Children's Hospital, we derived a dataset containing 256 PKU-suspected cases. We then developed a machine learning logistic regression analysis model with the aim to minimize false-positive rates in the results of the initial PKU test. The model attained a 95-100% sensitivity, the specificity was improved 53.14%, and positive predictive value increased from 19.14 to 32.16%. Our study shows that machine learning models may be used as a pediatric diagnosis aid tool to reduce the number of suspected cases and to help eliminate patient recall. Our study can serve as a future reference for the selection and evaluation of computational screening methods.

BackgroundIt is estimated that at present there are over 10 million rare disease patients in China. Recently an increased focus from policy perspective has been placed on rare diseases management. Improved disease definitions and the releases of local and national rare disease lists are some of the steps taken already. Despite these developments, few Chinese rare disease-related epidemiology and economic studies exist, thus hindering assessment of the true burden of rare diseases. For a rare disease with an effective treatment, this is a particularly important aspect due to the often-high cost associated.ObjectiveThe goal of this study is to address the data scarcity on the subject of rare diseases economic impact in China. We aim to address an existing knowledge gap and to provide a timely analysis of the economic burden of 23 rare diseases in Shanghai, China.MethodsWe utilized the data from the Health Information Exchange system of Shanghai and employed statistical modeling to analyze the economic burden of rare diseases with an effective treatment in Shanghai.ResultsFirst, we described the actual direct medical expenditure and analyzed its associated factors. Second, we found age, disease type, number of complications, and payment type were significantly associated with rare disease medical direct costs. Third, a generalized linear model was employed to estimate the annual direct cost. The mean direct medical cost was estimated as ¥9588 (US$1521) for inpatients and ¥1060 (US$168) for outpatients, and was over ¥15 million (~US$2.4 million) per year overall.ConclusionOur study is one of the first quantifying the economic burden of an extensive set of rare diseases in Shanghai and China. Our results can serve to inform healthcare-focused policy making, contribute to the increase of public awareness, and incentivize development of rare-disease strategies and treatments specific to the Chinese context.Electronic supplementary materialThe online version of this article (10.1186/s13023-019-1168-4) contains supplementary material, which is available to authorized users.

BackgroundSpinal muscular atrophy (SMA) is a rare neuromuscular disorder threating hundreds of thousands of lives worldwide. And the severity of SMA differs among different clinical types, which has been demonstrated to be modified by factors like SMN2, SERF1, NAIP, GTF2H2 and PLS3. However, the severities of many SMA cases, especially the cases within a family, often failed to be explained by these modifiers. Therefore, other modifiers are still waiting to be explored.Case presentationIn this study, we presented a rare case of SMA discordant family with a mild SMA male patient and a severe SMA female patient. The two SMA cases fulfilled the diagnostic criteria defined by the International SMA Consortium. With whole exome sequencing, we confirmed the heterozygous deletion of exon7 at SMN1 on the parents’ genomes and the homozygous deletions on the two patients’ genomes. The MLPA results confirmed the deletions and indicated that all the family members carry two copies of SMN2, SERF1, NAIP and GTF2H2. Further genomic analysis identified compound heterozygous mutations at TLL2 on the male patient’s genome, and compound heterozygous mutations at VPS13A and the de novo mutation at AGAP5 on female patient’s genome. TLL2 is an activator of myostatin, which negatively regulates the growth of skeletal muscle tissue. Mutation in TLL2 has been proved to increase muscular function in mice model. VPS13A encodes proteins that control the cycling of proteins through the trans-Golgi network to endosomes, lysosomes and the plasma membrane. And AGAP5 was reported to have GTPase activator activity.ConclusionsWe reported a case of SMA discordant family and identified mutations at TLL2, VPS13A and AGAP5 on the patients’ genomes. The mutations at TLL2 were predicted to be pathogenic and are likely to alleviate the severity of the male SMA patient. Our finding broadens the spectrum of genetic modifiers of SMA and will contribute to accurate counseling of SMA affected patients and families.