A Dual-Path Cross-Modal Network for Video-Music Retrieval.

In recent years, with the development of the internet, video has become more and more widely used in life. Adding harmonious music to a video is gradually becoming an artistic task. However, artificially adding music takes a lot of time and effort, so we propose a method to recommend background music for videos. The emotional message of music is rarely taken into account in current work, but it is crucial for video music retrieval. To achieve this, we design two paths to process content information and emotional information between modals. Based on the characteristics of video and music, we design various feature extraction schemes and common representation spaces. In the content path, the pre-trained network is used as the feature extraction network. As these features contain some redundant information, we use an encoder-decoder structure for dimensionality reduction. Where encoder weights are shared to obtain content sharing features for video and music. In the emotion path, an emotion key frames scheme was used for video and a channel attention mechanism was used for music in order to obtain the emotion information effectively. We also added emotion distinguish loss to guarantee that the network acquires the emotion information effectively. More importantly, we propose a way to combine content information with emotional information. That is, content features are first stitched together with sentiment features and then passed through a fused shared space structured as an MLP to obtain more effective fused shared features. In addition, a polarity penalty factor has been added to the classical metric loss function to make it more suitable for this task. Experiments show that this dual path video music retrieval network can effectively merge information. Compared with existing methods, the retrieval task evaluation index increases Recall@1 by 3.94.

Mobility Prediction Using a Weighted Markov Model Based on Mobile User Classification.

The vast amounts of mobile communication data collected by mobile operators can provide important insights regarding epidemic transmission or traffic patterns. By analyzing historical data and extracting user location information, various methods can be used to predict the mobility of mobile users. However, existing prediction algorithms are mainly based on the historical data of all users at an aggregated level and ignore the heterogeneity of individual behavior patterns. To improve prediction accuracy, this paper proposes a weighted Markov prediction model based on mobile user classification. The trajectory information of a user is extracted first by analyzing real mobile communication data, where the complexity of a user's trajectory is measured using the mobile trajectory entropy. Second, classification criteria are proposed based on different user behavior patterns, and all users are classified with machine learning algorithms. Finally, according to the characteristics of each user classification, the step threshold and the weighting coefficients of the weighted Markov prediction model are optimized, and mobility prediction is performed for each user classification. Our results show that the optimized weighting coefficients can improve the performance of the weighted Markov prediction model.

Improved Efficacy in a Fabry Disease Model Using a Systemic mRNA Liver Depot System as Compared to Enzyme Replacement Therapy.

Fabry disease is a lysosomal storage disorder caused by the deficiency of α-galactosidase A. Enzyme deficiency results in a progressive decline in renal and cardiac function, leading to cardiomyopathy and end-stage renal disease. Current treatments available, including enzyme replacement therapies, have provided significant benefit to patients; however, unmet medical needs remain. mRNA therapy, with drug-like properties, has the unique ability to produce therapeutic proteins endogenously. Here we describe the sustained delivery of therapeutic human α-galactosidase protein in vivo via nanoparticle-formulated mRNA in mouse and non-human primate, with a demonstration of efficacy through clinically relevant biomarker reduction in a mouse Fabry disease model. Multi-component nanoparticles formulated with lipids and lipid-like materials were developed for the delivery of mRNA encoding human α-galactosidase protein. Upon delivery of human GLA mRNA to mice, serum GLA protein levels reached as high as ∼1,330-fold over normal physiological values.

Should chromosomal microarray be offered to fetuses with ultrasonographic soft markers in second trimester: A prospective cohort study and meta-analysis.

OBJECTIVE: To evaluate whether chromosomal microarray (CMA) should be offered to fetuses with ultrasonographic soft markers (USMs) in the second trimester. METHODS: A prospective cohort study and meta-analysis were conducted. In the prospective cohort study, 564 fetuses with USMs were enrolled. In the meta-analysis, eligible articles describing copy number variations in fetuses with USMs were included. RESULTS: In the prospective cohort study, the diagnostic yields of CMA over non-invasive prenatal testing (NIPT) and karyotyping were significantly higher in fetuses with mild ventriculomegaly (MVM) than those in local control cohorts with normal ultrasound. However, the yields of CMA over NIPT and karyotyping in fetuses with other USMs were similar to controls. About ten studies, involving 405 fetuses with MVM and 1412 fetuses with other USMs, were included in the meta-analysis. The pooled diagnostic yields of CMA over NIPT and karyotyping in fetuses with MVM were 4.9% and 3.2%, respectively. In fetuses with other USMs, the yields of CMA over NIPT and karyotyping were 1.2% and 0.4%, respectively. CONCLUSION: It is reasonable to offer CMA as a first-tier test to fetuses with MVM. However, for fetuses with other USMs, the considerations to perform CMA should not differ from pregnancies with normal ultrasound.

Online 2D-LC-MS/MS assay to quantify therapeutic protein in human serum in the presence of pre-existing antidrug antibodies.

The formation of antidrug antibodies (ADA) can interfere with the accurate quantitation of therapeutic proteins, leading to significantly underestimated drug concentrations and confounded pharmacokinetic (PK) data interpretation. Although highly desirable, development of ADA-tolerant bioanalytical methods enabling unbiased measurement of both free and ADA-bound drug presents a considerable challenge. We report herein the development and validation of a robust LC-MS assay capable of quantifying therapeutic protein immunoglobulin A1 protease (IgAP) in human serum in the presence of pre-existing anti-IgAP antibodies. The procedure included sodium dodecyl sulfate (SDS) denaturation and chemical reduction of serum proteins to dissociate ADA-drug bindings, followed by tryptic digestion of protein pellets and subsequent LC-MS analysis of the surrogate IgAP peptide using stable isotope labeled peptide internal standard. Substantial enhancements in the sensitivity and selectivity were achieved by the combination of online two-dimensional reversed-phase LC (2D-LC) operated in high and low pH buffers, respectively, for efficient enrichment and quantitation of the surrogate peptide by multiple-reaction monitoring (MRM) mass spectrometry. Unlike ligand-binding assay, our method is not prone to interferences from ADA, allowing accurate and precise measurement of the IgAP in the range of 0.05 to 10 µg/mL in 25 µL of human serum with a wide range of anti-IgAP antibody levels. The intra- and inter-run precision (coefficient of variation (CV%)) was within 11.5% and 10.5%, respectively, and the bias was within ±7.1% for all quality control (QC) concentrations. With little modification, the described method can readily be applicable to the quantitation of other biotherapeutic proteins in the ADA-positive clinical matrices.

A chemical screen for biological small molecule-RNA conjugates reveals CoA-linked RNA.

Compared with the rapidly expanding set of known biological roles for RNA, the known chemical diversity of cellular RNA has remained limited primarily to canonical RNA, 3'-aminoacylated tRNAs, nucleobase-modified RNAs, and 5'-capped mRNAs in eukaryotes. We developed two methods to detect in a broad manner chemically labile cellular small molecule-RNA conjugates. The methods were validated by the detection of known tRNA and rRNA modifications. The first method analyzes small molecules cleaved from RNA by base or nucleophile treatment. Application to Escherichia coli and Streptomyces venezuelae RNA revealed an RNA-linked hydroxyfuranone or succinyl ester group, in addition to a number of other putative small molecule-RNA conjugates not previously reported. The second method analyzes nuclease-generated mononucleotides before and after treatment with base or nucleophile and also revealed a number of new putative small molecule-RNA conjugates, including 3'-dephospho-CoA and its succinyl-, acetyl-, and methylmalonyl-thioester derivatives. Subsequent experiments established that these CoA species are attached to E. coli and S. venezuelae RNA at the 5' terminus. CoA-linked RNA cannot be generated through aberrant transcriptional initiation by E. coli RNA polymerase in vitro, and CoA-linked RNA in E. coli is only found among smaller (approximately < 200 nucleotide) RNAs that have yet to be identified. These results provide examples of small molecule-RNA conjugates and suggest that the chemical diversity of cellular RNA may be greater than previously understood.

Granular Fuzzy Rule-Based Modeling With Incomplete Data Representation.

Incomplete data are frequently encountered and bring difficulties when it comes to further processing. The concepts of granular computing (GrC) help deliver a higher level of abstraction to address this problem. Most of the existing data imputation and related modeling methods are of numeric nature and require prior numeric models to be provided. The underlying objective of this study is to introduce a novel and straightforward approach that uses information granules as a vehicle to effectively represent missing data and build granular fuzzy models directly from resulting hybrid granular and numeric data. The evaluation and optimization of this method are guided by the principle of justifiable granularity engaging the coverage and specificity criteria and carried out with the help of particle swarm optimization. We provide a collection of experimental studies using a synthetic dataset and several publicly available real-world datasets to demonstrate the feasibility and analyze the main features of this method.

A Group-Based Distance Learning Method for Semisupervised Fuzzy Clustering.

Learning a proper distance for clustering from prior knowledge falls into the realm of semisupervised fuzzy clustering. Although most existing learning methods take prior knowledge (e.g., pairwise constraints) into account, they pay little attention to local knowledge of data, which, however, can be utilized to optimize the distance. In this article, we propose a novel distance learning method, which learns from the Group-level information, for semisupervised fuzzing clustering. We first present a new format of constraint information, called Group-level constraints, by elevating the pairwise constraints (must-links and cannot-links) from point level to Group level. The Groups, generated around data points contained in the pairwise constraints, carry not only the local information of data (the relation between close data points) but also more background information under some given limited prior knowledge. Then, we propose a novel method to learn a distance by using the Group-level constraints, namely, Group-based distance learning, in order to optimize the performance of fuzzy clustering. The distance learning process aims to pull must-link Groups as close as possible while pushing cannot-link Groups as far as possible. We formulate the learning process with the weights of constraints by invoking some linear and nonlinear transformations. The linear Group-based distance learning method is realized by means of semidefinite programming, and the nonlinear learning method is realized by using the neural network, which can explicitly provide nonlinear mappings. Experimental results based on both synthetic and real-world datasets show that the proposed methods yield much better performance compared to other distance learning methods using pairwise constraints.

Prenatal Diagnosis of Chromosomal Mosaicism in Over 18,000 Pregnancies: A Five-Year Single-Tertiary-Center Retrospective Analysis.

Background: Chromosomal mosaicism (CM) is a common biological phenomenon observed in humans. It is one of the main challenges in prenatal diagnosis due to uncertain outcomes, especially when fetal ultrasonographic features appear normal. This study aimed to assess the phenotypic features of CM detected during prenatal diagnosis and the risk factors affecting parents' pregnancy decisions. Materials and methods: A retrospective cohort study involving 18,374 consecutive pregnancies that underwent prenatal diagnosis by karyotyping, fluorescence in situ hybridization (FISH), or chromosome microarray analysis (CMA) was conducted. The association of risk factors with malformations detected by ultrasound and pregnancy outcomes was assessed using the chi-square test and binary logistic regression. Discordant results between the different methods were identified and further analyzed. Results: During this five-year period, 118 (0.6%) patients were diagnosed with CM. The incidences of CM in the chorionic villus, amniotic fluid, and umbilical cord blood were 3.2, 0.5, and 0.7%, respectively. The frequency of ultrasound malformations in individuals with a high fraction of autosomal CM was significantly higher than that in other groups (62.5% vs. 21.4-33.3%, all p <0.05). Inconsistent results between karyotyping and CMA/FISH were observed in 23 cases (19.5%). The risk of pregnancy termination in cases with ultrasound malformations, consistent results, autosomal CM, or a high CM fraction increased with an odds ratio of 3.09, 8.35, 2.30, and 7.62 (all p <0.05). Multiple regression analysis revealed that all four factors were independent risk factors for the termination of pregnancy. Conclusion: Patients with a high fraction of autosomal CM are more likely to have ultrasound malformations. Inconsistent results between different methods in CM are not rare. Ultrasound malformations, consistent results between different methods, autosomal CM, and a high CM fraction were independent risk factors for the choice to terminate pregnancies.

A dominant set-informed interpretable fuzzy system for automated diagnosis of dementia.

Dementia is an incurable neurodegenerative disease primarily affecting the older population, for which the World Health Organisation has set to promoting early diagnosis and timely management as one of the primary goals for dementia care. While a range of popular machine learning algorithms and their variants have been applied for dementia diagnosis, fuzzy systems, which have been known effective in dealing with uncertainty and offer to explicitly reason how a diagnosis can be inferred, sporadically appear in recent literature. Given the advantages of a fuzzy rule-based model, which could potentially result in a clinical decision support system that offers understandable rules and a transparent inference process to support dementia diagnosis, this paper proposes a novel fuzzy inference system by adapting the concept of dominant sets that arise from the study of graph theory. A peeling-off strategy is used to iteratively extract from the constructed edge-weighted graph a collection of dominant sets. Each dominant set is further converted into a parameterized fuzzy rule, which is finally optimized in a supervised adaptive network-based fuzzy inference framework. An illustrative example is provided that demonstrates the interpretable rules and the transparent reasoning process of reaching a decision. Further systematic experiments conducted on data from the Open Access Series of Imaging Studies (OASIS) repository, also validate its superior performance over alternative methods.

Identification of Fuzzy Rule-Based Models With Collaborative Fuzzy Clustering.

Fuzzy rule-based models (FRBMs) are sound constructs to describe complex systems. However, in reality, we may encounter situations, where the user or owner of a system only owns either the input or output data of that system (the other part could be owned by another user); and due to the consideration of data privacy, he/she could not obtain all the needed data to build the FRBMs. Since this type of situation has not been fully realized (noticed) and studied before, our objective is to come up with some strategy to address this challenge to meet the specific privacy consideration during the modeling process. In this study, the concept and algorithm of the collaborative fuzzy clustering (CFC) are applied to the identification of FRBMs, describing either multiple-input-single-output (MISO) or multiple-input-multiple-output (MIMO) systems. The collaboration between input and output spaces based on their structural information (conveyed in terms of the corresponding partition matrices) makes it possible to build FRBMs when input and output data could not be collected and used in unison. Surprisingly, on top of this primary pursuit, with the collaboration mechanism the input and output spaces of a system are endowed with an innovative way to comprehensively share, exchange, and utilize the structural information between each other, which results in their more relevant structures that guarantee better model performance compared with performance produced by some state-of-the-art modeling strategies. The effectiveness of the proposed approach is demonstrated by experiments on a series of synthetic and publicly available datasets.

LC/MS analysis of cellular RNA reveals NAD-linked RNA.

We developed a general method to detect cellular small molecule-RNA conjugates that does not rely on the reactivity of the small molecule. This technique revealed NAD-linked RNA in Escherichia coli and Streptomyces venezuelae. Subsequent characterization showed that NAD is a 5' modification of RNA, cannot be installed in vitro through aberrant transcriptional initiation, is only found among smaller cellular RNAs and is present at a surprisingly high abundance of approximately 3,000 copies per cell.

Approximation of Fuzzy Sets by Interval Type-2 Trapezoidal Fuzzy Sets.

In this paper, we propose a gradient-based method to approximate a fuzzy set through a trapezoidal fuzzy set (TFS). By adding some constraints in the formulated optimization problem, the major characteristics of the fuzzy set such as the core, the major part of the support, and the shape of the membership function could be preserved; also the form of the optimized result as a TFS is guaranteed. We regard the optimized TFS as the "skeleton" (blueprint) of the original fuzzy set. Based on this skeleton, we further extend the TFS to a higher type, that is, an interval type-2 TFS (IT2 TFS), so that more information about the original fuzzy set could be captured but the number of the parameters used to describe the original fuzzy set is still maintained low (nine parameters are required for an IT2 TFS). The principle of justifiable granularity is used to ensure that the formed type-2 information granule exhibits a sound interpretation. Both synthetic fuzzy sets and those constructed by the fuzzy C -means algorithm applied to the publicly available data have been used to demonstrate the usefulness of the proposed approximation methods.

Pharmacokinetics, Pharmacodynamics and Safety of Belimumab in Chinese Patients with Systemic Lupus Erythematosus: A Phase I, Open-Label Study.

INTRODUCTION: The B cell survival factor B lymphocyte stimulator (BLyS) is elevated in patients with systemic lupus erythematosus (SLE) and associated with disease activity. Belimumab, a monoclonal antibody specific for soluble BLyS, is approved for the treatment of adults with active, autoantibody-positive SLE receiving standard therapy. Ethnicity is one of the factors that can potentially affect the pharmacokinetics (PK) of therapeutic monoclonal antibodies, and therefore their efficacy and safety. METHODS: This phase 1, open-label study (200909) evaluated the pharmacokinetics (PK, primary objective), pharmacodynamics (PD), and safety (secondary objectives) of belimumab in Chinese patients with SLE (N = 20). Blood samples were taken up to 84 days after a single intravenous (IV) dose of belimumab 10 mg/kg. RESULTS: Peak serum concentrations of belimumab (Cmax) were obtained within the 1-h infusion. Geometric mean Cmax, area under the concentration-time curve (time 0 to last quantifiable concentration), terminal half-life, systemic clearance, and volume of distribution were 221 µg/mL, 2395 day·µg/mL, 14.6 days, 4.06 mL/day/kg, and 85.7 mL/kg, respectively. Decreases in CD20+, CD20+/CD27- naïve, CD20+/CD69+ active, CD20+/CD138+ plasmacytoid, CD19+/CD27BRIGHT/CD38BRIGHT SLE subset, and CD20-/CD138+ plasma B Cells post-dose were accompanied by an increase in CD20+/CD27+ memory B cells. Four cases of upper respiratory tract infection (three mild, one moderate) and one case of mild pharyngitis were possibly drug-related; all resolved during the study. CONCLUSION: PK of a single belimumab 10 mg/kg IV dose in Chinese patients with SLE were similar to those observed previously in Japanese and American (white and African-American) patients with SLE. PD results were consistent with belimumab inhibiting BLyS, and belimumab was well tolerated. These data support the use of belimumab in Chinese patients with SLE. TRIAL REGISTRATION: ClinicalTrials.gov identifier, NCT02880852.

Clustering Homogeneous Granular Data: Formation and Evaluation.

In this paper, we develop a comprehensive conceptual and algorithmic framework to cope with a problem of clustering homogeneous information granules. While there have been several approaches to coping with granular (viz. non-numeric) data, the origin of granular data themselves considered there is somewhat unclear and, as a consequence, the results of clustering start lacking some full-fledged interpretation. In this paper, we offer a holistic view at clustering information granules and an evaluation of the results of clustering. We start with a process of forming information granules with the use of the principle of justifiable granularity (PJG). With this regard, we discuss a number of parameters used in this development of information granules as well as quantify the quality of the granules produced in this manner. In the sequel, Fuzzy C -Means is applied to cluster the derived information granules, which are represented in a parametric manner and associated with weights resulting from the usage of the PJG. The quality of clustering results is evaluated through the use of the reconstruction criterion (quantifying the concept of information granulation and degranulation). A suite of experiments using synthetic and publicly available datasets is reported to quantify the performance of the proposed approach and highlight its key features.

Prenatal Diagnosis of Microdeletions or Microduplications in the Proximal, Central, and Distal Regions of Chromosome 22q11.2: Ultrasound Findings and Pregnancy Outcome.

Several recurrent microdeletions and microduplications in the proximal, central, and distal regions of chromosomal 22q11.2 have been identified. However, due to a limited number of patients reported in the literature, highly variable clinical phenotypes, and incomplete penetrance, the pathogenicity of some microdeletions/microduplications in 22q11.2 central and distal regions is unclear. Hence, the genetic counseling and subsequent pregnancy decision are extremely challenging, especially when they are found in structurally normal fetuses. Here, we reported 27 consecutive cases diagnosed prenatally with 22q11.2 microdeletions or microduplications by chromosomal microarray analysis in our center. The prenatal ultrasound features, inheritance of the microdeletions/microduplications, and their effects on the pregnancy outcome were studied. We found that fetuses with 22q11.2 microdeletions were more likely to present with structure defects in the ultrasound, as compared with fetuses with 22q11.2 microduplications. Both the prenatal ultrasound findings and the inheritance of the microdeletions/microduplications affected the parent's decision of pregnancy. Those with structure defects in prenatal ultrasound or occurred de novo often resulted in termination of the pregnancy, whereas those with normal ultrasound and inherited from healthy parent were likely to continue the pregnancy and led to normal birth. Our study emphasized that proximal, central, and distal 22q11.2 deletions or duplications were different from each other, although some common features were shared among them. More studies are warranted to demonstrate the underlying mechanisms of different clinical features of these recurrent copy-number variations, thereby to provide more information for genetic counseling of 22q11.2 microdeletions and microduplications when they are detected prenatally.

Translation of DNA into a library of 13,000 synthetic small-molecule macrocycles suitable for in vitro selection.

DNA-templated organic synthesis enables the translation, selection, and amplification of DNA sequences encoding synthetic small-molecule libraries. Previously we described the DNA-templated multistep synthesis and model in vitro selection of a pilot library of 65 macrocycles. In this work, we report several key developments that enable the DNA-templated synthesis of much larger (>10,000-membered) small-molecule libraries. We developed and validated a capping-based approach to DNA-templated library synthesis that increases final product yields, simplifies the structure and preparation of reagents, and reduces the number of required manipulations. To expand the size and structural diversity of the macrocycle library, we augmented the number of building blocks in each DNA-templated step from 4 to 12, selected 8 different starting scaffolds which result in 4 macrocycle ring sizes and 2 building-block orientations, and confirmed the ability of the 36 building blocks and 8 scaffolds to generate DNA-templated macrocycle products. We computationally generated and experimentally validated an expanded set of codons sufficient to support 1728 combinations of step 1, step 2, and step 3 building blocks. Finally, we developed new high-resolution LC/MS analysis methods to assess the quality of large DNA-templated small-molecule libraries. Integrating these four developments, we executed the translation of 13,824 DNA templates into their corresponding small-molecule macrocycles. Analysis of the resulting libraries is consistent with excellent (>90%) representation of desired macrocycle products and a stringent test of sequence specificity suggests a high degree of sequence fidelity during translation. The quality and structural diversity of this expanded DNA-templated library provides a rich starting point for the discovery of functional synthetic small-molecule macrocycles.

Construction of SH-EP1-alpha4beta2-hAPP695 cell line and effects of nicotinic agonists on beta-amyloid in the cells.

(1) Nicotinic acetylcholine receptors in central nervous system are thought to be new targets for Alzheimer's disease. However, the most involved nicotinic receptor subtype in Alzheimer's disease is unclear. alpha4beta2 receptor is the most widely spread subtype in brain, involving in several important aspects of cognitive and other functions. We constructed cell line by transfecting human amyloid precursor protein (695) gene into SH-EP1 cells which have been transfected with human nicotinic receptor alpha4 subunit and beta2 subunit gene, to observe effects of alpha4beta2 receptors activation on beta-amyloid, expecting to provide a new cell line for drug screening and research purpose. (2) Liposome transfection was used to express human amyloid precursor protein (695) gene in SH-EP1-alpha4beta2 cells. Function of the transfected alpha4beta2 receptors was tested by patch clamp. Effects of nicotine and epibatidine (selective alpha4beta2 nicotinic receptor agonist) on beta-amyloid were detected by Western blot and ELISA. Effects of nicotine and epibatidine on amyloid precursor protein (695) mRNA level were measured using real-time PCR. (3) Human amyloid precursor protein (695) gene was stably expressed in SH-EP1-alpha4beta2 cells; Nicotine (1 muM) and epibatidine (0.1 muM) decreased intracellular and secreted beta-amyloid in the cells; and activation of alpha4beta2 receptors did not affect amyloid precursor protein (695) mRNA level. (4) These results suggest that the constructed cell line, expressing both amyloid precursor protein (695) gene and human nicotinic receptor alpha4 subunit and beta2 subunit gene, might be useful for screening specific nicotinic receptor agonists against Alzheimer's disease. Alteration of Abeta level induced by activation of alpha4beta2 nAChR in our study might occur at a post-translational level.

Targeted sequencing identifies a novel SH2D1A pathogenic variant in a Chinese family: Carrier screening and prenatal genetic testing.

X-linked lymphoproliferative disease type 1 (XLP1) is a rare primary immunodeficiency characterized by a clinical triad consisting of severe EBV-induced hemophagocytic lymphohistiocytosis, B-cell lymphoma, and dysgammaglobulinemia. Mutations in SH2D1A gene have been revealed as the cause of XLP1. In this study, a pregnant woman with recurrence history of birthing immunodeficiency was screened for pathogenic variant because the proband sample was unavailable. We aimed to clarify the genetic diagnosis and provide prenatal testing for the family. Next-generation sequencing (NGS)-based multigene panel was used in carrier screening of the pregnant woman. Variants of immunodeficiency related genes were analyzed and prioritized. Candidate variant was verified by using Sanger sequencing. The possible influence of the identified variant was evaluated through RNA assay. Amniocentesis, karyotyping, and Sanger sequencing were performed for prenatal testing. We identified a novel de novo frameshift SH2D1A pathogenic variant (c.251_255delTTTCA) in the pregnant carrier. Peripheral blood RNA assay indicated that the mutant transcript could escape nonsense-mediated mRNA decay (NMD) and might encode a C-terminal truncated protein. Information of the variant led to success prenatal diagnosis of the fetus. In conclusion, our study clarified the genetic diagnosis and altered disease prevention for a pregnant carrier of XLP1.

An open-label clinical trial of agalsidase alfa enzyme replacement therapy in children with Fabry disease who are naïve to enzyme replacement therapy.

BACKGROUND: Following a drug manufacturing process change, safety/efficacy of agalsidase alfa were evaluated in enzyme replacement therapy (ERT)-naïve children with Fabry disease. METHODS: In an open-label, multicenter, Phase II study (HGT-REP-084; Shire), 14 children aged ≥7 years received 0.2 mg/kg agalsidase alfa every other week for 55 weeks. Primary endpoints: safety, changes in autonomic function (2-hour Holter monitoring). Secondary endpoints: estimated glomerular filtration rate, left ventricular mass index (LVMI), midwall fractional shortening, pharmacodynamic parameters, and patient-reported quality-of-life. RESULTS: Among five boys (median 10.2 [range 6.7, 14.4] years) and nine girls (14.8 [10.1, 15.9] years), eight patients experienced infusion-related adverse events (vomiting, n=4; nausea, n=3; dyspnea, n=3; chest discomfort, n=2; chills, n=2; dizziness, n=2; headache, n=2). One of these had several hypersensitivity episodes. However, no patient discontinued for safety reasons and no serious adverse events occurred. One boy developed immunoglobulin G (IgG) and neutralizing antidrug antibodies. Overall, no deterioration in cardiac function was observed in seven patients with low/abnormal SDNN (standard deviation of all filtered RR intervals; <100 ms) and no left ventricular hypertrophy: mean (SD) baseline SDNN, 81.6 (20.9) ms; mean (95% confidence interval [CI]) change from baseline to week 55, 17.4 (2.9, 31.9) ms. Changes in SDNN correlated with changes in LVMI (r=-0.975). No change occurred in secondary efficacy endpoints: mean (95% CI) change from baseline at week 55 in LVMI, 0.16 (-3.3, 3.7) g/m(2.7); midwall fractional shortening, -0.62% (-2.7%, 1.5%); estimated glomerular filtration rate, 0.15 (-11.4, 11.7) mL/min/1.73 m(2); urine protein, -1.8 (-6.0, 2.4) mg/dL; urine microalbumin, 0.6 (-0.5, 1.7) mg/dL; plasma globotriaosylceramide (Gb3), -5.71 (-10.8, -0.6) nmol/mL; urinary Gb3, -1,403.3 (-3,714.0, 907.4) nmol/g creatinine, or clinical quality-of-life outcomes. CONCLUSION: Fifty-five weeks' agalsidase alfa ERT at 0.2 mg/kg every other week was well tolerated. Disease progression may be slowed when ERT is started prior to major organ dysfunction. TRIAL REGISTRATION: https://ClinicalTrials.gov identifier NCT01363492.