Management of mother-to-child transmission of hepatitis B virus: Propositions and challenges

AbstractChronic hepatitis B virus (HBV) infection due to mother-to-child transmission (MTCT) during perinatal period remains an important global health problem. Despite standard passive–active immunoprophylaxis with hepatitis B immunoglobulin (HBIG) and hepatitis B vaccine in neonates, up to 9% of newborns still acquire HBV infection, especially these from hepatitis B e antigen (HBeAg) positive mothers. Management of HBV infection in pregnancy still need to draw careful attention because of some controversial aspects, including the failure of passive-active immunoprophylaxis in a fraction of newborns, the effect and necessity of periodical hepatitis B immunoglobulin (HBIG) injection to the mothers, the safety of antiviral prophylaxis with nucleoside/nucleotide analogs, the benefit of different delivery ways, and the safety of breastfeeding. In this review, we highlight these unsettled issues of preventive strategies in perinatal period, and we further aim to provide an optimal approach to the management of preventing MTCT of HBV infection

Boosting Unsupervised Contrastive Learning Using Diffusion-Based Data Augmentation From Scratch

Unsupervised contrastive learning methods have recently seen significant improvements, particularly through data augmentation strategies that aim to produce robust and generalizable representations. However, prevailing data augmentation methods, whether hand designed or based on foundation models, tend to rely heavily on prior knowledge or external data. This dependence often compromises their effectiveness and efficiency. Furthermore, the applicability of most existing data augmentation strategies is limited when transitioning to other research domains, especially science-related data. This limitation stems from the paucity of prior knowledge and labeled data available in these domains. To address these challenges, we introduce DiffAug-a novel and efficient Diffusion-based data Augmentation technique. DiffAug aims to ensure that the augmented and original data share a smoothed latent space, which is achieved through diffusion steps. Uniquely, unlike traditional methods, DiffAug first mines sufficient prior semantic knowledge about the neighborhood. This provides a constraint to guide the diffusion steps, eliminating the need for labels, external data/models, or prior knowledge. Designed as an architecture-agnostic framework, DiffAug provides consistent improvements. Specifically, it improves image classification and clustering accuracy by 1.6%~4.5%. When applied to biological data, DiffAug improves performance by up to 10.1%, with an average improvement of 5.8%. DiffAug shows good performance in both vision and biological domains.Comment: arXiv admin note: text overlap with arXiv:2302.07944 by other author

Computed Tomography-Based Radiomics in Predicting T Stage and Length of Esophageal Squamous Cell Carcinoma

Background: Because of the superficial and infiltrative spreading patterns of esophageal squamous cell carcinoma (ESCC), an accurate assessment of tumor extent is challenging using imaging-based clinical staging. Radiomics features extracted from pretreatment computed tomography (CT) or magnetic resonance imaging have shown promise in identifying tumor characteristics. Accurate staging is essential for planning cancer treatment, especially for deciding whether to offer surgery or radiotherapy (chemotherapy) in patients with locally advanced ESCC. Thus, this study aimed to evaluate the predictive potential of contrast-enhanced CT-based radiomics as a non-invasive approach for estimating pathological tumor extent in ESCC patients. Methods: Patients who underwent esophagectomy between October 2011 and September 2017 were retrospectively studied and included 116 patients with pathologically confirmed ESCC. Contrast-enhanced CT from the neck to the abdomen was performed in all patients during the 2 weeks before the operation. Radiomics features were extracted from segmentations, which were contoured by radiologists. Cluster analysis was performed to obtain clusters with similar radiomics characteristics, and chi-squared tests were used to assess differences in clinicopathological features and survival among clusters. Furthermore, a least absolute shrinkage and selection operator was performed to select radiomics features and construct a radiomics model. Receiver operating characteristic analysis was used to evaluate the predictive ability of the radiomics signatures. Results: All 116 ESCC patients were divided into two groups according to the cluster analysis. The chi-squared test showed that cluster-based radiomics features were significantly correlated with T stage (p = 0.0254) and tumor length (p = 0.0002). Furthermore, CT radiomics signatures exhibited favorable predictive performance for T stage (area under the curve [AUC] = 0.86, sensitivity = 0.77, and specificity = 0.87) and tumor length (AUC = 0.95, sensitivity = 0.92, and specificity = 0.91). Conclusions: CT contrast radiomics is a simple and non-invasive method that shows promise for predicting pathological T stage and tumor length preoperatively in ESCC patients and may aid in the accurate assessments of patients in combination with the existing examinations

Effects of 8-Year Nitrogen and Phosphorus Treatments on the Ecophysiological Traits of Two Key Species on Tibetan Plateau

Understanding how nitrogen (N) and/or phosphorus (P) addition affects plants carbon- and water- related ecophysiological characteristics is essential for predicting the global change impact on the alpine meadow ecosystem structure and function in carbon and water cycling. The Qinghai-Tibetan Plateau (QTP) with the largest alpine meadow in the world is regarded as the third pole in the earth and has been experiencing increased atmospheric N deposition. In this project, we focused on two key species (Elymus dahuricus and Gentiana straminea) of the alpine meadow on the Tibetan Plateau and investigated the variability of photosynthetic and stomatal responses to 8-year N and/or P treatments through field measurements and modeling. We measured photosynthesis- and gs-response curves to generate parameter estimates from individual leaves with two widely used stomatal models (the BWB model and MED model) for validation of growth and ecosystem models and to elucidate the physiological basis for observed differences in productivity and WUE. We assessed WUE by means of gas exchange measurements (WUEi) and stable carbon isotope composition (Δ13C) to get the intrinsic and integrated estimates of WUE of the two species. P and N+P treatments, but not N, improved the photosynthetic capacity (Anet and Vcmax) for both species. Stomatal functions including instaneous measurements of stomatal conductance, intrinsic water-use efficiency and stomatal slope parameters of the two widely used stomatal models were altered by the addition of P or N+P treatment, but the impact varied across years and species. The inconsistent responses across species suggest that an understanding of photosynthetic, stomatal functions and water-use should be evaluated on species separately. WUE estimated by Δ13C values had a positive relationship with Anet and gs and a negative relationship with WUEi. Our findings should be useful for understanding the underlying mechanisms of the response of alpine plants growth and alpine meadow ecosystem to global change

m6A Regulator-Based Exosomal Gene Methylation Modification Patterns Identify Distinct Microenvironment Characterization and Predict Immunotherapeutic Responses in Colon Cancer.

peer reviewedRecent studies have highlighted the biological significance of exosomes and m6A modifications in immunity. Nonetheless, it remains unclear whether the m6A modification gene in exosomes of body fluid has potential roles in the tumor microenvironment (TME). Herein, we identified three different m6A-related exosomal gene modification patterns based on 59 m6A-related exosomal genes, which instructed distinguishing characteristics of TME in colon cancer (CC). We demonstrated that these patterns could predict the stage of tumor inflammation, subtypes, genetic variation, and patient prognosis. Furthermore, we developed a scoring mode-m6A-related exosomal gene score (MREGS)-by detecting the level of m6A modification in exosomes to classify immune phenotypes. Low MREGS, characterized by prominent survival and immune activation, was linked to a better response to anti-PDL1 immunotherapy. In contrast, the higher MREGS group displayed remarkable stromal activation, high activity of innate immunocytes, and a lower survival rate. Hence, this work provides a novel approach for evaluating TME cell infiltration in colon cancer and guiding more effective immunotherapy strategies

Corrigendum to: The TianQin project: current progress on science and technology

In the originally published version, this manuscript included an error related to indicating the corresponding author within the author list. This has now been corrected online to reflect the fact that author Jun Luo is the corresponding author of the article

What Caused the Sharp Downturn in the Chinese Economy during the Global Financial Crisis? A Critical Note on Causality in Trade Linkage

In responding to a view that attributes sharp downturns in the Chinese economy in late 2008 and early 2009 to the "collapse of external demand," the present paper scrutinizes three relevant issues: How have large Chinese importers behaved in a demand-price setting? How have Chinese commodity imports and exports interacted in recent years? Did the downturns in China's export growth come earlier and were they deeper than those in Chinese import growth? All answers appear to suggest a conclusion contrary to the abovementioned view: sharp downturns in China's trade and economy during the recent global financial crisis were, to a large extent, caused by certain domestic factors, or by factors that should not be regarded as entirely "external." Insomuch as globalization has advanced, a large economy like China's today faces new potential sources of macroeconomic disturbances, from inside and outside. Copyright (c) 2010 The Authors Journal compilation (c) 2010 Institute of World Economics and Politics, Chinese Academy of Social Sciences.

Improving object-oriented land use/cover classification from high resolution imagery by spectral similarity-based post-classification

To classify an image, traditional classifiers depend mainly on the spectral and/or textural distinctions between different land-cover units, while this study attempts to explore the properties of statistical distinction. Using the historical classification results, we present a novel algorithm for imagery classification that achieves high accuracy, automation and efficiency. Based on object-oriented image analysis, it exploits the advantages of dch (the Chaudhuri’s metric) using a multi-step approach, and the objective is not to reclassify an image, but to refine or update the existing land-use/-cover classification results by comparing the pairwise dch value (namely similarity) between different image segments. Finally, the similar/homogeneous segments will be confirmed as their original class labels, while the inhomogeneous/dissimilar segments will be masked out with an appropriate threshold on the similarity image and be relabelled. We have systematically evaluated the algorithm by running it on the basis of the existing GIS base maps, which indicated the good performance of it

Extracting tea plantations in complex landscapes using Sentinel-2 imagery and machine learning algorithms

Tea tree is an economically important crop. The rapid and efficient mapping of the distribution and dynamic changes in tea plantations informs decision making for government departments. It also plays an important role in rational tea planting and environmental governance. This study used the 10 m Sentinel-2 image data to map the spatial distribution of tea plantations in Yingde City, China. In this article, we analyzed the differences in vegetation and texture characteristics between the new and mature tea plantations. We found that the texture features of the new and mature tea plantations were significantly different in contrast, which can be used as an index for tea plantation extraction. Moreover, we selected machine learning classifiers, including support vector machine (SVM) and random forests (RF) method, which were utilized to extract the preliminary classification to complete spatial distribution mapping of tea plantations. The overall accuracy of SVM and RF was 90.79% and 89.42%, respectively, and the kappa coefficient was 0.88 and 0.86, respectively. SVM had the highest overall accuracy in terms of tea plantation distribution at the regional scale. These results demonstrate that: (1) separating tea plantations into mature and new tea plantations, taking into account vegetation and texture features such as soil brightness index and contrast, will help improve the accuracy of tea plantation classification and (2) using multi-period images combined with machine learning classification methods can improve the efficiency and accuracy of tea plantation identification