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We reported a rare ß-thalassemia patient, a 41-year-old Chinese male with small cell hypopigmentation anemia, jaundice and splenomegaly as the main clinical symptoms. By using Next-Generation Sequencing (NGS), we identified a novel de novo HBB mutation(c.358_365dup, p.Phe123Alafs*39) which resulted in an abnormally prolonged ß-globin chain comprising 159 amino acid residues. The secondary and three-dimensional structures of the ß-globin predicted that the novel prolonged ß-globin chain has a considerable risk of instability in the hemoglobin, and leads to clinical phenotype. This study contributes to the enrichment of the genetic pathogenic mutation database for thalassemia and underscores the significance of NGS in the screening of mutations for thalassemia families.

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Detecting salient objects in complicated scenarios is a challenging problem. Except for semantic features from the RGB image, spatial information from the depth image also provides sufficient cues about the object. Therefore, it is crucial to rationally integrate RGB and depth features for the RGB-D salient object detection task. Most existing RGB-D saliency detectors modulate RGB semantic features with absolution depth values. However, they ignore the appearance contrast and structure knowledge indicated by relative depth values between pixels. In this work, we propose a depth-induced network (DIN) for RGB-D salient object detection, to take full advantage of both absolute and relative depth information, and further, enforce the in-depth fusion of the RGB-D cross-modalities. Specifically, an absolute depth-induced module (ADIM) is proposed, to hierarchically integrate absolute depth values and RGB features, to allow the interaction between the appearance and structural information in the encoding stage. A relative depth-induced module (RDIM) is designed, to capture detailed saliency cues, by exploring contrastive and structural information from relative depth values in the decoding stage. By combining the ADIM and RDIM, we can accurately locate salient objects with clear boundaries, even from complex scenes. The proposed DIN is a lightweight network, and the model size is much smaller than that of state-of-the-art algorithms. Extensive experiments on six challenging benchmarks, show that our method outperforms most existing RGB-D salient object detection models.

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BACKGROUND: Key opinion leaders (KOLs) are people who can influence public opinion on a certain subject matter. In the field of medical and health informatics, it is critical to identify KOLs on various disease conditions. However, there have been very few studies on this topic. OBJECTIVE: We aimed to develop a recommender system for identifying KOLs for any specific disease with health care data mining. METHODS: We exploited an unsupervised aggregation approach for integrating various ranking features to identify doctors who have the potential to be KOLs on a range of diseases. We introduce the design, implementation, and deployment details of the recommender system. This system collects the professional footprints of doctors, such as papers in scientific journals, presentation activities, patient advocacy, and media exposure, and uses them as ranking features to identify KOLs. RESULTS: We collected the information of 2,381,750 doctors in China from 3,657,797 medical journal papers they published, together with their profiles, academic publications, and funding. The empirical results demonstrated that our system outperformed several benchmark systems by a significant margin. Moreover, we conducted a case study in a real-world system to verify the applicability of our proposed method. CONCLUSIONS: Our results show that doctors' profiles and their academic publications are key data sources for identifying KOLs in the field of medical and health informatics. Moreover, we deployed the recommender system and applied the data service to a recommender system of the China-based Internet technology company NetEase. Patients can obtain authority ranking lists of doctors with this system on any given disease.

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OBJECTIVE: To discuss the changes of lymphocyte subsets in HCV children with different genotypes during treatment with pegylated interferon alfa-2b and ribavirin. METHODS: The genotype of 45 HCV infected children were identified by real time PCR. The lymphocyte subsets were dynamically detected by BD FACSCalibur flow cytometer with four color MultiTEST IMK Kit during the treatment. RESULTS: For the children with 1b genotype, after 24 weeks, the CD4+ T cells were higher than pre-treatment (P < 0.05). For the children with 2a genotype, after 12 weeks and after 24 weeks, the CD3+ T cells and CD4+ T cells significantly increased while the NK cells decreased than pre-treatment (P < 0.05). CONCLUSIONS: The lymphocyte subsets of HCV children with 2a genotype were different from 1b genotype during trentment with pegylated interferon alfa-2b and ribavirin.

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