Clinical application of targeted next-generation sequencing in severe pneumonia: a retrospective review.

BACKGROUND: The precise identification of the underlying causes of infectious diseases, such as severe pneumonia, is essential, and the development of next-generation sequencing (NGS) has enhanced the effectiveness of pathogen detection. However, there is limited information on the systematic assessment of the clinical use of targeted next-generation sequencing (tNGS) in cases of severe pneumonia. METHODS: A retrospective analysis was conducted on 130 patients with severe pneumonia treated in the ICU from June 2022 to June 2023. The consistency of the results of tNGS, metagenomics next-generation sequencing (mNGS), and culture with the clinical diagnosis was evaluated. Additionally, the results for pathogens detected by tNGS were compared with those of culture, mNGS, and quantitative reverse transcription PCR (RT-qPCR). To evaluate the efficacy of monitoring severe pneumonia, five patients with complicated infections were selected for tNGS microbiological surveillance. The tNGS and culture drug sensitisation results were then compared. RESULTS: The tNGS results for the analysis of the 130 patients showed a concordance rate of over 70% with clinical diagnostic results. The detection of pathogenic microorganisms using tNGS was in agreement with the results of culture, mNGS, and RT-qPCR. Furthermore, the tNGS results for pathogens in the five patients monitored for complicated infections of severe pneumonia were consistent with the culture and imaging test results during treatment. The tNGS drug resistance results were in line with the drug sensitivity results in approximately 65% of the cases. CONCLUSIONS: The application of tNGS highlights its promise and significance in assessing the effectiveness of clinical interventions and providing guidance for anti-infection therapies for severe pneumonia.

A Word-Granular Adversarial Attacks Framework for Causal Event Extraction.

As a data augmentation method, masking word is commonly used in many natural language processing tasks. However, most mask methods are based on rules and are not related to downstream tasks. In this paper, we propose a novel masking word generator, named Actor-Critic Mask Model (ACMM), which can adaptively adjust the mask strategy according to the performance of downstream tasks. In order to demonstrate the effectiveness of the method, we conducted experiments on two causal event extraction datasets. Experiment results show that, compared with various rule-based masking methods, the masked sentences generated by our proposed method can significantly enhance the generalization of the model and improve the model performance.

p53 induces miR-199a-3p to suppress mechanistic target of rapamycin activation in cisplatin-induced acute kidney injury.

How p53 participates in acute kidney injury (AKI) progress and what are the underlying mechanisms remain illusive. For this issue, it is important to probe into the role of p53 in cisplatin-induced AKI. We find that p53 was upregulated in cisplatin-induced AKI, yet, pifithrin-α inhibites the p53 expression to attenuated renal injury and cell apoptosis both in vivo cisplatin-induced AKI mice and in vitro HK-2 human renal tubular epithelial cells. To knock down p53 by siRNA significantly decreased the miRNA, miR-199a-3p, expression in HK-2 cells. Blockade of miR-199a-3p significantly reduced cisplatin-induced cell apoptosis and inhibited caspase-3 activity. Mechanistically, we identified that miR-199a-3p directly bound to mechanistic target of rapamycin (mTOR) 3'-untranslated region and overexpressed miR-199a-3p reduce the expression and phosphorylation of mTOR. Furthermore, we demonstrated that p53 inhibited mTOR activation through activating miR-199a-3p. In conclusion, our findings reveal that p53, upregulating the expression of miR-199a-3p affects the progress of cisplatin-induced AKI, which might provide a promising therapeutic target of AKI.

A novel approach to word sense disambiguation based on topical and semantic association.

Word sense disambiguation (WSD) is a fundamental problem in nature language processing, the objective of which is to identify the most proper sense for an ambiguous word in a given context. Although WSD has been researched over the years, the performance of existing algorithms in terms of accuracy and recall is still unsatisfactory. In this paper, we propose a novel approach to word sense disambiguation based on topical and semantic association. For a given document, supposing that its topic category is accurately discriminated, the correct sense of the ambiguous term is identified through the corresponding topic and semantic contexts. We firstly extract topic discriminative terms from document and construct topical graph based on topic span intervals to implement topic identification. We then exploit syntactic features, topic span features, and semantic features to disambiguate nouns and verbs in the context of ambiguous word. Finally, we conduct experiments on the standard data set SemCor to evaluate the performance of the proposed method, and the results indicate that our approach achieves relatively better performance than existing approaches.

Adaptive Deep Modeling of Users and Items Using Side Information for Recommendation.

In the existing recommender systems, matrix factorization (MF) is widely applied to model user preferences and item features by mapping the user-item ratings into a low-dimension latent vector space. However, MF has ignored the individual diversity where the user's preference for different unrated items is usually different. A fixed representation of user preference factor extracted by MF cannot model the individual diversity well, which leads to a repeated and inaccurate recommendation. To this end, we propose a novel latent factor model called adaptive deep latent factor model (ADLFM), which learns the preference factor of users adaptively in accordance with the specific items under consideration. We propose a novel user representation method that is derived from their rated item descriptions instead of original user-item ratings. Based on this, we further propose a deep neural networks framework with an attention factor to learn the adaptive representations of users. Extensive experiments on Amazon data sets demonstrate that ADLFM outperforms the state-of-the-art baselines greatly. Also, further experiments show that the attention factor indeed makes a great contribution to our method.

Low Cost Edge Sensing for High Quality Demosaicking.

Digital cameras that use Color Filter Arrays (CFA) entail a demosaicking procedure to form full RGB images. To digital camera industry, demosaicking speed is as important as demosaicking accuracy, because camera users have been accustomed to viewing captured photos instantly. Moreover, the cost associated with demosaicking should not go beyond the cost saved by using CFA. For this purpose, we revisit the classical Hamilton-Adams (HA) algorithm, which outperforms many sophisticated techniques in both speed and accuracy. Our analysis shows that the HA pipeline is highly efficient to exploit the originally captured data, but its oversimplified inter- and intra-channel smoothness formulation hinders its accuracy. We therefore propose a very low cost edge sensing scheme, which guides demosaicking by a logistic functional of the difference between directional variations. We extensively compare our algorithm with 27 demosaicking algorithms by running their open source codes on benchmark datasets. Compared to methods of similar computational cost, our method achieves substantially higher accuracy; Whereas compared to methods of similar accuracy, our method has significantly lower cost. On test images of currently popular resolution, the quality of our algorithm is comparable to top performers, yet our speed is tens of times faster. Source code for this work will be released with paper publication.

Effect of bosentan is correlated with MMP-9/TIMP-1 ratio in bleomycin-induced pulmonary fibrosis.

Pulmonary fibrosis (PF) is a life-threatening non-tumorous disease characterized by progressive fibrosis and worsening lung function. Various drugs, such as bleomycin, can contribute to lung injury and PF, with lung injury potentially occurring in 10% of bleomycin users. Bleomycin is the most commonly used drug in the establishment of an animal model of PF in rats. Matrix metalloproteinases (MMPs) and tissue inhibitors of metalloproteinases (TIMPs) serve an important role in controlling tissue organization and fibrosis following injury. The present study examined the effect of bosentan on fibrotic lung tissue in rats administrated with bleomycin. In total, 48 Wistar rats were administrated with bleomycin, with or without bosentan, while the control rats received saline. The lung tissues were microscopically examined by staining with hematoxylin and eosin and Masson's trichome. ELISA was also used to detect the MMP-9 and TIMP-1 concentrations in the plasma. The results indicated that the bosentan-treated groups on the next day and the 15th day showed significant reversal of pathological findings. In addition, the concentrations of MMP-9 and TIMP-1 appeared to be altered following bosentan treatment, improving the bleomycin-induced PF. Masson's trichome staining showed high collagen deposition in the lung tissue sections, which may be a direct result of the activity of MMP-9 and TIMP-1. Furthermore, the deposition of collagen was significantly inhibited in bosentan-treated groups. In conclusion, these results demonstrated that bosentan inhibited lung fibrosis induced by bleomycin and it may be used as an inhibitor of PF.

A Multilayer Naïve Bayes Model for Analyzing User's Retweeting Sentiment Tendency.

Today microblogging has increasingly become a means of information diffusion via user's retweeting behavior. Since retweeting content, as context information of microblogging, is an understanding of microblogging, hence, user's retweeting sentiment tendency analysis has gradually become a hot research topic. Targeted at online microblogging, a dynamic social network, we investigate how to exploit dynamic retweeting sentiment features in retweeting sentiment tendency analysis. On the basis of time series of user's network structure information and published text information, we first model dynamic retweeting sentiment features. Then we build Naïve Bayes models from profile-, relationship-, and emotion-based dimensions, respectively. Finally, we build a multilayer Naïve Bayes model based on multidimensional Naïve Bayes models to analyze user's retweeting sentiment tendency towards a microblog. Experiments on real-world dataset demonstrate the effectiveness of the proposed framework. Further experiments are conducted to understand the importance of dynamic retweeting sentiment features and temporal information in retweeting sentiment tendency analysis. What is more, we provide a new train of thought for retweeting sentiment tendency analysis in dynamic social networks.

[Relationship of the expression of CD44v6 and paxillin to the prognosis of non-small cell lung carcinoma].

OBJECTIVE: To investigate the expression and significance of CD44v6 and Paxillin in non-small cell lung carcinoma. METHODS: Immunohistochemical staining was used to detect the expression of CD44v6 and Paxillin in 79 cases of non-small cell lung carcinoma, and the relationships of the expression with histological type, lymph node metastasis and prognosis were detected. RESULTS: The expression rates of CD44v6 and Paxillin were 60.8% and 34.1% respectively. The expression of CD44v6 was positively correlated with metastasis and negatively correlated with the 2-year survival rate (P < 0.05). The expression of Paxillin was positively correlated with metastasis (P < 0.05). CONCLUSION: The expression of CD44v6 and Paxillin is closely correlated with the lymph node metastasis and the prognosis of non-small cell lung carcinoma.