Efficacy of consensus interferon in treatment of HbeAg-positive chronic hepatitis B: a multicentre, randomized controlled trial

<p>Abstract</p> <p>Background</p> <p>Consensus interferon (CIFN) is a newly developed type I interferon.</p> <p>Aims</p> <p>This multicentre, controlled trial was conducted to determine the efficacy of CIFN and to compare it with alpha-1b-interferon (IFN-α1b) in the treatment of patients with hepatitis B e antigen (HBeAg)-positive chronic hepatitis B.</p> <p>Methods</p> <p>144 Patients were randomly assigned to receive 9 μg CIFN (CIFN group) or 50 μg INF-α1b (IFN-alpha group) subcutaneously 3 times weekly for 24 weeks, followed by 24 weeks of observation. Efficacy was assessed by normalization of serum alanine transaminase (ALT) levels and the non-detectability of serum hepatitis B virus DNA or HBeAg at the end of treatment and 24 weeks after stopping treatment.</p> <p>Results</p> <p>There was no statistically significant difference in the serological, virological and biochemical parameters between CIFN and IFN-α1b groups at the end of the therapy and follow-up period (p > 0.05). Overall, at the end of treatment, 7.0% (5/71) and 35.2% (25/71) of patients in the CIFN group showed a complete or partial response compared with 7.4% (5/68) and 33.8% (23/68) of the IFN-alpha group (p = 0.10). At 24 weeks after stopping treatment, 6.9% (5/72) and 37.5% (27/72) of patients in the CIFN group showed complete response or partial response compared with 7.1% (5/70) and 34.3% (24/70) of the IFN-alpha group (p = 0.10).</p> <p>Conclusion</p> <p>These findings suggest that 9 μg CIFN is effective in the treatment of patients with HBeAg-positive chronic hepatitis B. It can gradually induce ALT normalization and HBV DNA clearance and HBeAg loss or HBeAg/HBeAb seroconversion.</p

TwinTex: Geometry-aware Texture Generation for Abstracted 3D Architectural Models

Coarse architectural models are often generated at scales ranging from individual buildings to scenes for downstream applications such as Digital Twin City, Metaverse, LODs, etc. Such piece-wise planar models can be abstracted as twins from 3D dense reconstructions. However, these models typically lack realistic texture relative to the real building or scene, making them unsuitable for vivid display or direct reference. In this paper, we present TwinTex, the first automatic texture mapping framework to generate a photo-realistic texture for a piece-wise planar proxy. Our method addresses most challenges occurring in such twin texture generation. Specifically, for each primitive plane, we first select a small set of photos with greedy heuristics considering photometric quality, perspective quality and facade texture completeness. Then, different levels of line features (LoLs) are extracted from the set of selected photos to generate guidance for later steps. With LoLs, we employ optimization algorithms to align texture with geometry from local to global. Finally, we fine-tune a diffusion model with a multi-mask initialization component and a new dataset to inpaint the missing region. Experimental results on many buildings, indoor scenes and man-made objects of varying complexity demonstrate the generalization ability of our algorithm. Our approach surpasses state-of-the-art texture mapping methods in terms of high-fidelity quality and reaches a human-expert production level with much less effort. Project page: https://vcc.tech/research/2023/TwinTex.Comment: Accepted to SIGGRAPH ASIA 202

Power-law cosmological solution derived from DGP brane with a brane tachyon field

By studying a tachyon field on the DGP brane model, in order to embed the 4D standard Friedmann equation with a brane tachyon field in 5D bulk, the metric of the 5D spacetime is presented. Then, adopting the inverse square potential of tachyon field, we obtain an expanding universe with power-law on the brane and an exact 5D solution.Comment: 8 pages, 1 figure, accepted by IJMP

Deep Learning-Based Multi-Omics Data Integration Reveals Two Prognostic Subtypes in High-Risk Neuroblastoma

High-risk neuroblastoma is a very aggressive disease, with excessive tumor growth and poor outcomes. A proper stratification of the high-risk patients by prognostic outcome is important for treatment. However, there is still a lack of survival stratification for the high-risk neuroblastoma. To fill the gap, we adopt a deep learning algorithm, Autoencoder, to integrate multi-omics data, and combine it with K-means clustering to identify two subtypes with significant survival differences. By comparing the Autoencoder with PCA, iCluster, and DGscore about the classification based on multi-omics data integration, Autoencoder-based classification outperforms the alternative approaches. Furthermore, we also validated the classification in two independent datasets by training machine-learning classification models, and confirmed its robustness. Functional analysis revealed that MYCN amplification was more frequently occurred in the ultra-high-risk subtype, in accordance with the overexpression of MYC/MYCN targets in this subtype. In summary, prognostic subtypes identified by deep learning-based multi-omics integration could not only improve our understanding of molecular mechanism, but also help the clinicians make decisions

RDAD: A Machine Learning System to Support Phenotype-Based Rare Disease Diagnosis

DNA sequencing has allowed for the discovery of the genetic cause for a considerable number of diseases, paving the way for new disease diagnostics. However, due to the lack of clinical samples and records, the molecular cause for rare diseases is always hard to identify, significantly limiting the number of rare Mendelian diseases diagnosed through sequencing technologies. Clinical phenotype information therefore becomes a major resource to diagnose rare diseases. In this article, we adopted both a phenotypic similarity method and a machine learning method to build four diagnostic models to support rare disease diagnosis. All the diagnostic models were validated using the real medical records from RAMEDIS. Each model provides a list of the top 10 candidate diseases as the prediction outcome and the results showed that all models had a high diagnostic precision (≥98%) with the highest recall reaching up to 95% while the models with machine learning methods showed the best performance. To promote effective diagnosis for rare disease in clinical application, we developed the phenotype-based Rare Disease Auxiliary Diagnosis system (RDAD) to assist clinicians in diagnosing rare diseases with the above four diagnostic models. The system is freely accessible through http://www.unimd.org/RDAD/

Statefinder Parameters for Interacting Phantom Energy with Dark Matter

We apply in this paper the statefinder parameters to the interacting phantom energy with dark matter. There are two kinds of scaling solutions in this model. It is found that the evolving trajectories of these two scaling solutions in the statefinder parameter plane are quite different, and that are also different from the statefinder diagnostic of other dark energy models.Comment: 9 pages, 12 figures, some references are added, some words are modifie

Development and characterization of BAC-end sequence derived SSRs, and their incorporation into a new higher density genetic map for cultivated peanut (Arachis hypogaea L.)

<p>Abstract</p> <p>Background</p> <p>Cultivated peanut (<it>Arachis hypogaea </it>L.) is an important crop worldwide, valued for its edible oil and digestible protein. It has a very narrow genetic base that may well derive from a relatively recent single polyploidization event. Accordingly molecular markers have low levels of polymorphism and the number of polymorphic molecular markers available for cultivated peanut is still limiting.</p> <p>Results</p> <p>Here, we report a large set of BAC-end sequences (BES), use them for developing SSR (BES-SSR) markers, and apply them in genetic linkage mapping. The majority of BESs had no detectable homology to known genes (49.5%) followed by sequences with similarity to known genes (44.3%), and miscellaneous sequences (6.2%) such as transposable element, retroelement, and organelle sequences. A total of 1,424 SSRs were identified from 36,435 BESs. Among these identified SSRs, dinucleotide (47.4%) and trinucleotide (37.1%) SSRs were predominant. The new set of 1,152 SSRs as well as about 4,000 published or unpublished SSRs were screened against two parents of a mapping population, generating 385 polymorphic loci. A genetic linkage map was constructed, consisting of 318 loci onto 21 linkage groups and covering a total of 1,674.4 cM, with an average distance of 5.3 cM between adjacent loci. Two markers related to resistance gene homologs (RGH) were mapped to two different groups, thus anchoring 1 RGH-BAC contig and 1 singleton.</p> <p>Conclusions</p> <p>The SSRs mined from BESs will be of use in further molecular analysis of the peanut genome, providing a novel set of markers, genetically anchoring BAC clones, and incorporating gene sequences into a linkage map. This will aid in the identification of markers linked to genes of interest and map-based cloning.</p