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Drug-induced liver injury (DILI) is an important adverse drug reaction that can lead to acute liver failure or even death in severe cases. Currently, the diagnosis of DILI still follows the strategy of exclusion. Therefore, a detailed history taking and a thorough and careful exclusion of other potential causes of liver injury is the key to correct diagnosis. This guideline was developed based on evidence-based medicine provided by the latest research advances and aims to provide professional guidance to clinicians on how to identify suspected DILI timely and standardize the diagnosis and management in clinical practice. Based on the clinical settings in China, the guideline also specifically focused on DILI in chronic liver disease, drug-induced viral hepatitis reactivation, common causing agents of DILI (herbal and dietary supplements, anti-tuberculosis drugs, and antineoplastic drugs), and signal of DILI in clinical trials and its assessment. [ABSTRACT FROM AUTHOR]

Background: There is growing recognition of natural history, complications, and outcomes of patients who develop non-acetaminophen (APAP) drug-induced acute liver failure (ALF). To clarify high-risk factors and develop a nomogram model to predict transplant-free survival (TFS) in patients with non-APAP drug-induced ALF. Methods: Patients with non-APAP drug-induced ALF from 5 participating centers were retrospectively analyzed. The primary endpoint was 21-day TFS. Total sample size was 482 patients. Results: Regarding causative agents, the most common implicated drugs were herbal and dietary supplements (HDS) (57.0%). The hepatocellular type (R ≥ 5) was the main liver injury pattern (69.0%). International normalized ratio, hepatic encephalopathy grades, the use of vasopressor, N-acetylcysteine, or artificial liver support system were associated with TFS and incorporated to construct a nomogram model (drug-induced acute liver failure-5, DIALF-5). The AUROC of DIALF-5 for 7-day, 21-day, 60-day, and 90-day TFS in the internal cohort were 0.886, 0.915, 0.920, and 0.912, respectively. Moreover, the AUROC of DIALF-5 for 21-day TFS had the highest AUROC, which was significantly higher than 0.725 of MELD and 0.519 of KCC (p < 0.05), numerically higher than 0.905 of ALFSG-PI but without statistical difference (p > 0.05). These results were successfully validated in the external cohort (147 patients). Conclusions: Based on easily identifiable clinical data, the novel DIALF-5 model was developed to predict transplant-free survival in non-APAP drug-induced ALF, which was superior to KCC, MELD and had a similar prediction performance to ALFSG-PI but is more convenient, which can directly calculate TFS at multiple time points. [ABSTRACT FROM AUTHOR]

Image-Recipe retrieval is the task of retrieving closely related recipes from a collection given a food image and vice versa. The modality gap between images and recipes makes it a challenging task. Recent studies usually focus on learning consistent image and recipe representations to bridge the semantic gap. Though the existing methods have significantly improved image-recipe retrieval, several challenges still remain: 1) Previous studies usually directly concatenate the textual embeddings of different recipe components to generate recipe presentations. Only simple interactions rather than complex interactions are considered. 2) They commonly focus on textual feature extraction from recipes. The methods to extract image features are relatively simple, and most studies utilize the ResNet-50 model. 3) Apart from the retrieval learning loss (triplet loss, for example), several auxiliary loss functions (such as adversarial loss and reconstruction loss) are commonly used to match the recipe and image representations. To deal with these issues, we introduce a novel Low-rank Multi-component Fusion method with Component-Specific Factors (LMF-CSF) to model the different textual components in a recipe for producing superior textual representations. Furthermore, try to pay some attention to image feature extraction. A visual transformer is used to learn better image representations. Then the enhanced representations from two modalities are directly fed into a triplet loss function for image-recipe retrieval learning. Experimental results conducted on the Recipe1M dataset indicate that our LMF-CSF method can outperform the current state-of-the-art image-recipe retrieval baselines. [ABSTRACT FROM AUTHOR]

Non-alcoholic fatty liver disease is a growing health burden with limited treatment options worldwide. Herein we report a randomized, double-blind, placebo-controlled, multiple-dose trial of a first-in-class pan-phosphodiesterase inhibitor ZSP1601 in 36 NAFLD patients (NCT04140123). There were three cohorts. Each cohort included twelve patients, nine of whom received ZSP1601 50 mg once daily, 50 mg twice daily, or 100 mg twice daily, and three of whom received matching placebos for 28 days. The primary outcomes were the safety and tolerability of ZSP1601. A total of 27 (27/36, 75%) patients experienced at least one treatment-emergent adverse event (TEAE). Most TEAEs were mild to moderate. There was no Serious Adverse Event. Diarrhea, transiently elevated creatinine and adaptive headache were frequently reported adverse drug reaction. We conclude that ZSP1601 is well-tolerated and safe, showing effective improvement in liver chemistries, liver fat content and fibrosis in patients with NAFLD. Non-alcoholic fatty liver disease is a growing health burden with limited treatment options worldwide. Herein the authors report a randomized, double-blind, placebo-controlled, multiple-dose trial of a first-in-class pan-phosphodiesterase inhibitor ZSP1601 in NAFLD patients. [ABSTRACT FROM AUTHOR]

Leaves and flowers of Amorphophallus konjac do not develop simultaneously thus unique features can be elucidated through study of flowering transformation in A. konjac. In this study, transcriptome libraries of A. konjac leaf buds (LB) and flower buds (FB) were constructed followed by high-throughput sequencing. A total of 68,906 unigenes with an average length of 920 bp were obtained after library assembly. Out of these genes, 24,622 unigenes had annotation information. A total of 6859 differentially expressed genes (DEGs) were identified through differential expression analysis using LB as control. Notably, 2415 DEGs were upregulated whereas 4444 DEGs were downregulated in the two transcriptomes. Go and KEGG analysis showed that the DEGs belonged to 44 functional categories and were implicated in 98 metabolic pathways and 38 DEGs involved in plant hormone signal transduction. Several genes were mined that may be involved in A. konjac flower bud differentiation and flower organ development. Eight DEGs were selected for verification of RNA-seq results using qRT-PCR analysis. Two FLOWERING LOCUS T (FT) genes named AkFT1 and AkFT2 were identified though homologous analysis may be the florigen gene implicated in modulation of A. konjac flowering. These genes were significantly upregulated in flower buds compared with the expression levels on leaf buds. Overexpression of AkFT genes though heterologous expression in Arabidopsis showed that the transgenics flowered at a very early stage relative to wild type plants. These findings indicate that AkFT1 and AkFT2 function as regulation genes in A. konjac flowering development and the two genes may present similar functions during flowering transition. [ABSTRACT FROM AUTHOR]

There exist communication barriers between the deaf people and the listeners. Sign language translation is a reasonable and effective way to break these barriers. Recognition of sign language symbols is an essential part of sign language translation. Sign language digits of (0–9) and alphabetic letters of (A–Z) are elementary but important symbols of sign languages of different countries or regions. Capsule networks (CapsNet) are promising alternative to convolutional neural networks (CNN), which take into account of the spatial relationships and orientations of the features of an entity. For sign language digits and alphabets recognition tasks, the proposed SLR-CapsNet architecture achieves a start-of-the-art test accuracy of 99.52% with 100*100 RGB input size and 99.94% with 32*32 RGB input size on Sign Language Digits Dataset and 99.60% with 28*28 Gray-scale input on Sign Language MNIST Dataset. The experimental results also prove that CapsNet has higher generalization and expressiveness capacity on unseen data than CNN dose. Another important finding in our work is that SLR-CapsNet is robust to routing iterations, i.e., its performance will not be affected by various routing iterations. [ABSTRACT FROM AUTHOR]

Integrating cognitive radio (CR) with traditional wireless networks is helping solve the problem of spectrum scarcity in an efficient manner. The opportunistic and dynamic spectrum access features of CR provide the functionality to its unlicensed users to utilize the underutilized spectrum at the time of need because CR nodes can sense vacant bands of spectrum and can also access them to carry out communication. Various capabilities of CR nodes depend upon efficient and continuous reporting of data with each other and centralized base stations, which in turn can cause leakage in privacy. Experimental studies have shown that the privacy of CR users can be compromised easily during the cognition cycle, because they are knowingly or unknowingly sharing various personally identifiable information (PII), such as location, device ID, signal status, etc. In order to preserve this privacy leakage, various privacy preserving strategies have been developed by researchers, and according to us differential privacy is the most significant among them. In this article, we provide a thorough survey on how differential privacy can play an active role in preserving privacy of cognitive radio networks (CRN). Firstly, we provide a thorough comparison of our work with other similar studies to show its novelty and contribution, and afterwards, we provide a thorough analysis from the perspective of various CR scenarios which can cause privacy leakage. After that, we carry out an in-depth assessment from the perspective of integration of differential privacy at different levels of CRN. Then, we discuss various parameters which should be considered while integrating differential privacy in CRN alongside providing a comprehensive discussion about all integrations of differential privacy carried out till date. Finally, we provide discussion about prospective applications, challenges, and future research directions. The discussion about integration of differential privacy in different CR scenarios indicates that differential privacy is one of the most viable mechanisms to preserve privacy of CRN in modern day scenarios. From the discussion in the article, it is evident that the proposed integration of differential privacy can pave the way for futuristic CRN in which CR users will be able to share information during the cognition cycle without the risk of losing their private information. [ABSTRACT FROM AUTHOR]

Background: No reports exist regarding the prevalence of different Na levels and their relationship with 90-day prognosis in hospitalized patients with acute-on-chronic liver disease (AoCLD) in China. Therefore, the benefit of hyponatremia correction in AoCLD patients remains unclear. Methods: We prospectively collected the data of 3970 patients with AoCLD from the CATCH-LIFE cohort in China. The prevalence of different Na levels (≤ 120; 120–135; 135–145; > 145) and their relationship with 90-day prognosis were analyzed. For hyponatremic patients, we measured Na levels on days 4 and 7 and compared their characteristics, based on whether hyponatremia was corrected. Results: A total of 3880 patients were involved; 712 of those developed adverse outcomes within 90 days. There were 80 (2.06%) hypernatremic, 28 (0.72%) severe hyponatremic, and 813 (20.95%) mild hyponatremic patients at admission. After adjusting for all confounding factors, the risk of 90-day adverse outcomes decreased by 5% (odds ratio [OR] 0.95; 95% confidence interval [CI] 0.93–0.97; p < 0.001), 24% (OR 0.76; 95% CI 0.70–0.84; p < 0.001), and 42% (OR 0.58; 95% CI 0.49–0.70; p < 0.001) as Na level increased by 1, 5, and 10 mmol/L, respectively. Noncorrection of hyponatremia on days 4 and 7 was associated with 2.05-fold (hazard ratio [HR], 2.05; 95% CI, 1.50–2.79; p < 0.001) and 1.46-fold (HR 1.46; 95% CI 1.05–2.02; p = 0.028) higher risk of adverse outcomes. Conclusions: Hyponatremia was an independent risk factor for a poor 90-day prognosis in patients with AoCLD. Failure to correct hyponatremia in a week after admission was often associated with increased mortality. (ClinicalTrials.gov number: NCT02457637, NCT03641872). Clinical Trial Numbers: This study is registered at Shanghai www.clinicaltrials.org (NCT02457637 and NCT03641872). [ABSTRACT FROM AUTHOR]