Post-transplant hepatitis B virus reactivation impacts the prognosis of patients with hepatitis B-related hepatocellular carcinoma: a dual-centre retrospective cohort study in China.

BACKGROUND: Highly active hepatitis B virus (HBV) is known to be associated with poor outcomes in patients with hepatocellular carcinoma (HCC). This study aims to investigate the relationship between HBV status and HCC recurrence after liver transplantation. METHODS: The study retrospectively analyzed HCC patients undergoing liver transplantation in two centres between January 2015 and December 2020. The authors reviewed post-transplant HBV status and its association with outcomes. RESULTS: The prognosis of recipients with hepatitis B surface antigen (HBsAg) reappearance ( n =58) was poorer than those with HBsAg persistent negative ( n =351) and positive ( n =53). In HBsAg persistent positive group, recipients with HBV DNA reappearance or greater than 10-fold increase above baseline had worse outcomes than those without ( P <0.01). HBV reactivation was defined as (a) HBsAg reappearance or (b) HBV DNA reappearance or greater than 10-fold increase above baseline. After propensity score matching, the 5-year overall survival rate and recurrence-free survival rate after liver transplantation in recipients with HBV reactivation were significantly lower than those without (32.0% vs. 62.3%; P <0.01, and 16.4% vs. 63.1%; P <0.01, respectively). Moreover, HBV reactivation was significantly related to post-transplant HCC recurrence, especially lung metastasis. Cox regression analysis revealed that beyond Milan criteria, microvascular invasion and HBsAg-positive graft were independent risk factors for post-transplant HBV reactivation, and a novel nomogram was established accordingly with a good predictive efficacy (area under the time-dependent receiver operating characteristic curve=0.78, C-index =0.73). CONCLUSIONS: Recipients with HBV reactivation had worse outcomes and higher tumour recurrence rates than those without. The nomogram could be used to evaluate the risk of post-transplant HBV reactivation effectively.

Probing the decelerating trajectory of a Raman soliton using temporal reflection.

Temporal reflection is a process where an optical pulse reflects off a moving boundary with different refractive indices across it. In a dispersive medium, this process creates a reflected pulse with a frequency shift that changes its speed. Such frequency shifts depend on the speed of the moving boundary. In this work, we propose and experimentally show that it is possible to probe the trajectory of the boundary by measuring the frequency shifts while changing the initial delay between the incident pulse and the boundary. We demonstrate this effect by reflecting a probe pulse off a short soliton, acting as a moving boundary that decelerates inside a photonic crystal fiber because of intrapulse Raman scattering. We deduce trajectory of the soliton from the measured spectral data for the reflected pulse.

Extracting Chinese events with a joint label space model.

The task of event extraction consists of three subtasks namely entity recognition, trigger identification and argument role classification. Recent work tackles these subtasks jointly with the method of multi-task learning for better extraction performance. Despite being effective, existing attempts typically treat labels of event subtasks as uninformative and independent one-hot vectors, ignoring the potential loss of useful label information, thereby making it difficult for these models to incorporate interactive features on the label level. In this paper, we propose a joint label space framework to improve Chinese event extraction. Specifically, the model converts labels of all subtasks into a dense matrix, giving each Chinese character a shared label distribution via an incrementally refined attention mechanism. Then the learned label embeddings are also used as the weight of the output layer for each subtask, hence adjusted along with model training. In addition, we incorporate the word lexicon into the character representation in a soft probabilistic manner, hence alleviating the impact of word segmentation errors. Extensive experiments on Chinese and English benchmarks demonstrate that our model outperforms state-of-the-art methods.

Impact of the boundary's sharpness on temporal reflection in dispersive media.

We investigate the impact of the finite rise time of a spatiotemporal boundary inside a dispersive medium used for reflection and refraction of optical pulses. We develop a matrix approach in the frequency domain for analyzing such spatiotemporal boundaries and use it to show that the frequency range over which reflection can occur is reduced as the rise time increases. We also show that total internal reflection can occur even for boundaries with long rise times. This feature suggests that spatiotemporal waveguides can be realized through cross-phase modulation even when pump pulses have relatively long rise and fall times.

Correction: A transition-based neural framework for Chinese information extraction.

[This corrects the article DOI: 10.1371/journal.pone.0235796.].

A message-passing multi-task architecture for the implicit event and polarity detection.

Implicit sentiment analysis is a challenging task because the sentiment of a text is expressed in a connotative manner. To tackle this problem, we propose to use textual events as a knowledge source to enrich network representations. To consider task interactions, we present a novel lightweight joint learning paradigm that can pass task-related messages between tasks during training iterations. This is distinct from previous methods that involve multi-task learning by simple parameter sharing. Besides, a human-annotated corpus with implicit sentiment labels and event labels is scarce, which hinders practical applications of deep neural models. Therefore, we further investigate a back-translation approach to expand training instances. Experiment results on a public benchmark demonstrate the effectiveness of both the proposed multi-task architecture and data augmentation strategy.

Assessment of Landslide Susceptibility Based on Multiresolution Image Segmentation and Geological Factor Ratings.

Evaluating the susceptibility of regional landslides is one of the core steps in spatial landslide prediction. Starting from multiresolution image segmentation and object-oriented classification theory, this paper uses the four parameters of entropy, energy, correlation, and contrast from remote-sensing images in the Zigui-Badong section of Three Gorges Reservoir as image texture factors; the original image data for the study area were divided into 2279 objects after segmentation. According to the various indicators of the existing historical landslide database in the Three Gorges Reservoir area, combined with the classification processing steps for different types of multistructured data, the relevant geological evaluation factors, including the slope gradient, slope structure, and engineering rock group, were rated based on expert experience. From the perspective of the object-oriented segmentation of multiresolution images and geological factor rating classification, the C5.0 decision tree susceptibility classification model was constructed for the prediction of four types of landslide susceptibility units in the Zigui-Badong section. The mapping results show that the engineering rock group of a high-susceptibility unit usually develops in soft rock or soft-hard interphase rock groups, and the slope is between 15°-30°. The model results show that the average accuracy is 91.64%, and the kappa coefficients are 0.84 and 0.51, indicating that the C5.0 decision tree algorithm provides good accuracy and can clearly divide landslide susceptibility levels for a specific area, respectively. This landslide susceptibility classification, based on multiresolution image segmentation and geological factor classification, has potential applicability.

A transition-based neural framework for Chinese information extraction.

Chinese information extraction is traditionally performed in the process of word segmentation, entity recognition, relation extraction and event detection. This pipelined approach suffers from two limitations: 1) It is prone to introduce propagated errors from upstream tasks to subsequent applications; 2) Mutual benefits of cross-task dependencies are hard to be introduced in non-overlapping models. To address these two challenges, we propose a novel transition-based model that jointly performs entity recognition, relation extraction and event detection as a single task. In addition, we incorporate subword-level information into character sequence with the use of a hybrid lattice structure, removing the reliance of external word tokenizers. Results on standard ACE benchmarks show the benefits of the proposed joint model and lattice network, which gives the best result in the literature.

Topic-informed neural approach for biomedical event extraction.

As a crucial step of biological event extraction, event trigger identification has attracted much attention in recent years. Deep representation methods, which have the superiorities of less feature engineering and end-to-end training, show better performance than statistical methods. While most deep learning methods have been done on sentence-level event extraction, there are few works taking document context into account, losing potentially informative knowledge that is beneficial for trigger detection. In this paper, we propose a variational neural approach for biomedical event extraction, which can take advantage of latent topics underlying documents. By adopting a joint modeling manner of topics and events, our model is able to produce more meaningful and event-indicative words compare to prior topic models. In addition, we introduce a language model embeddings to capture context-dependent features. Experimental results show that our approach outperforms various baselines in a commonly used multi-level event extraction corpus.

An improved synthesis of telmisartan via the copper-catalyzed cyclization of o-haloarylamidines.

A concise synthetic route was designed for making telmisartan. The key bis-benzimidazole structure was constructed via the copper-catalyzed cyclization of o-haloarylamidines. By adopting this approach, telmisartan was obtained in a 7-step overall yield of 54% starting from commercially available 3-methyl-4-nitrobenzoic acid, and the use of HNO3/H2SO4 for nitration and polyphosphoric acid (PPA) for cyclization in the reported literatures were avoided.

Nature brings new avenues to the therapy of central nervous system diseases-An overview of possible treatments derived from natural products.

Natural products (NPs), including traditional Chinese medicine (TCM), have been long and widely used in the prevention and treatment of central nervous system (CNS) diseases by virtue of their abundant sources, diverse structures, and novel activities. In this review article, we intend to summarize and discuss the situation or status of the clinical employments or trials of the NPs and their derivatives with CNS activities. NPs that have been extensively studied in preclinical research in recent years are also included. The compounds presented in this review are classified according to their indications and followed by details such as natural sources, possible biological mechanisms, and development status, while a considerable proportion of them are found in TCM. In addition, some drug combinations with synergistic effects are also mentioned. According to their impressive therapeutic effects and novel chemical structures, NPs are not only effective therapeutic remedies in clinic, but also lead compounds for structural modification, which indicate that nature brings new avenues to the therapy of CNS diseases.

Synthesis and biological evaluation of a series of novel pyridinecarboxamides as potential multi-receptor antipsychotic drugs.

In previous study, a series of benzamides was identified as potent antipsychotic agents. As a continuation of the program to discover novel antipsychotics, herein we reported the evaluation of a series of pyridinecarboxamide derivatives. The most promising compound 7h not only held good activities on dopamine D2, serotonin 5-HT1A and 5-HT2A receptors, but also exhibited low potency for α1A, H1 and 5-HT2C receptors, indicating a low propensity of side effects like orthostatic hypotension and weight gain. Furthermore, 7h exhibited more potent antipsychotic-like effect than aripiprazole in behavioral studies. The preliminary results were promising enough for further research around this scaffold.

Synthesis and biological evaluation of a series of multi-target N-substituted cyclic imide derivatives with potential antipsychotic effect.

In the present study, a series of multi-target N-substituted cyclic imide derivatives which possessed potent dopamine D2, serotonin 5-HT1A and 5-HT2A receptors properties were synthesized and evaluated as potential antipsychotics. Among these compounds, (3aR,4R,7S,7aS)-2-(4-(4-(benzo[b]thiophen-4-yl)piperazin-1-yl)butyl)-3a,4,7,7a-tetrahydro-1H-4,7-methanoisoindole-1,3(2H)-dione hydrochloride (3d) held a promising pharmacological profile. 3d not only showed potent and balanced in vitro activities on D2/5-HT1A/5-HT2A receptors, but also endowed with low to moderate activities on 5-HT2C, H1, α1A, M3 receptors and hERG channel, suggesting a low liability to induce side effects such as weight gain, orthostatic hypotension and QT prolongation. In animal behavioral studies, 3d reduced phencyclidine-induced hyperlocomotion with a high threshold for catalepsy induction. Compound 3d was selected as a potential antipsychotic candidate for further development.

Application of packed porous nanofibers-solid-phase extraction for the detection of sulfonamide residues from environmental water samples by ultra high performance liquid chromatography with mass spectrometry.

Porous electrospun nanofibers, as new materials for solid-phase extraction, were synthesized by electrospinning and coupled with ultra high performance liquid chromatography and mass spectrometry to determine sulfonamide residues in environmental water. Aligned porous polystyrene electrospun nanofibers were fabricated under the mechanism of phase separation. The high-specific surface of these nanofibers (70 m(2)/g) could improve recoveries of the target sulfonamides 4-10 times compared with that of polystyrene nonporous material (3.8 m(2)/g). Under the optimized conditions, 13 sulfonamide residues showed an excellent linear relationship in the range of 0.125-12.5 ng/mL with a linear correlation coefficient (r(2)) greater than 0.99, and the detection limits of sulfonamides were as low as 0.80-5.0 ng/L. Compared to the commercial C18 and HLB columns, the homemade porous nanofibers columns had some merits including simple fabrication and extraction process, short process time and environmental friendliness. The optimized method was applied to eight water samples collected from different livestock farms (Xuzhou, China). The results showed that polystyrene porous nanofibers were promising to preconcentrate sulfonamides of different polarities in the waste water.