Ryanodine receptor 1 (RYR1) gene mutations are associated with central core disease (CCD), multiminicore disease (MmD) and malignant hyperthermia (MH), and have been reported to be responsible for 47-67% of patients with CCD and rare cases with MmD. However, to date, the true frequency and distribution of the mutations along the RYR1 gene have not been determined yet, since mutation screening has been limited to three 'hot spots', with particular attention to the C-terminal region. In this study, 27 unrelated Japanese CCD patients were included. Clinical histories and muscle biopsies were carefully reviewed. We sequenced all the 106 exons encoding RYR1 with their flanking exon-intron boundaries, and identified 20 novel and 3 previously reported heterozygous missense mutations in 25 of the 27 CCD patients (93%), which is a much higher mutation detection rate than that perceived previously. Among them, six were located outside the known 'hot spots'. Sixteen of 27 (59%) CCD patients had mutations in the C-terminal 'hot spot'. Three CCD patients had a probable autosomal recessive disease with two heterozygous mutations. Patients with C-terminal mutations had earlier onset and rather consistent muscle pathology characterized by the presence of distinct cores in almost all type 1 fibres, interstitial fibrosis and type 2 fibre deficiency. In contrast, patients with mutations outside the C-terminal region had milder clinical phenotype and harbour more atypical cores in their muscle fibres. We also sequenced two genes encoding RYR1-associated proteins as candidate causative genes for CCD: the 12 kD FK506-binding protein (FKBP12) and the alpha1 subunit of L-type voltage-dependent calcium channel or dihydropyridine receptor (CACNA1S). However, no mutation was found, suggesting that these genes may not, or only rarely, be responsible for CCD. Our results indicate that CCD may be caused by RYR1 mutations in the majority of patients.

The mutational spectrum and primary clinical features of patients with CADASIL from mainland China are similar to those in Caucasians. However, migraine with aura and abnormal white matter in the temporal pole are less common than among Caucasians, while brainstem involvement is more common than among Caucasians.

BackgroundEpidemiological data on the prevalence of headache in nursing staff in Mainland China are lacking. We therefore performed a study to assess the prevalence of headache, and factors associated with headaches, in nursing staff in three hospitals in North China.MethodsStratified random cluster sampling was used to select 1102 nurses from various departments in three hospitals. A structured questionnaire was used to collect epidemiological data, headache characteristics and associated factors.ResultsThe response rate was 93.0%. Among nursing staff, the 1-year prevalence of primary headache disorders was 45.3%, of migraine 14.8% (migraine with aura 3.4%, migraine without aura 11.4%), of tension-type headache (TTH) 26.2%, of chronic daily headache (CDH) 2.7%. Multivariate analysis showed that seniority (≥5 years) was a risk factor for migraine (OR 2.280), obesity (BMI ≥ 25) was a risk factor for TTH and CDH (OR 1.684 and 3.184), and age (≥40 years) was a risk factor for CDH (OR 8.455). Nurses working in internal medicine were more likely to suffer CDH than those in other departments. Working a greater number of night shifts was also associated with increased prevalence of headache.ConclusionThe prevalence of primary headache disorders in nurses is higher than that in the general population in China, and occupational factors may play an important role. Therefore, the prevalence of headache in nurses should be a focus of attention, and coping strategies should be provided. Such measures could contribute to improving patient care.

With the explosive growth of online information, recommender systems play a key role to alleviate such information overload. Due to the important application value of recommender system, there have always been emerging works in this field. In recent years, graph neural network (GNN) techniques have gained considerable interests which can naturally integrate node information and topological structure. Owing to the outperformance of GNN in learning on graph data, GNN methods have been widely applied in many fields. In recommender systems, the main challenge is to learn the efficient user/item embeddings from their interactions and side information if available. Since most of the information essentially has graph structure and GNNs have superiority in representation learning, the field of utilizing graph neural network in recommender systems is flourishing. This article aims to provide a comprehensive review of recent research efforts on graph neural network based recommender systems. Specifically, we provide a taxonomy of graph neural network based recommendation models and state new perspectives pertaining to the development of this field.

Congenital neuromuscular disease with uniform type 1 fiber (CNMDU1) in 40% of patients is associated with mutations in the C-terminal domain of RYR1, suggesting that CNMDU1 is allelic to central core disease at least in some patients.