Mechanistic prediction and validation of Brevilin A Therapeutic effects in Lung Cancer.

BACKGROUND: Traditional Chinese medicine (TCM) has been found widespread application in neoplasm treatment, yielding promising therapeutic candidates. Previous studies have revealed the anti-cancer properties of Brevilin A, a naturally occurring sesquiterpene lactone derived from Centipeda minima (L.) A.Br. (C. minima), a TCM herb, specifically against lung cancer. However, the underlying mechanisms of its effects remain elusive. This study employs network pharmacology and experimental analyses to unravel the molecular mechanisms of Brevilin A in lung cancer. METHODS: The Batman-TCM, Swiss Target Prediction, Pharmmapper, SuperPred, and BindingDB databases were screened to identify Brevilin A targets. Lung cancer-related targets were sourced from GEO, Genecards, OMIM, TTD, and Drugbank databases. Utilizing Cytoscape software, a protein-protein interaction (PPI) network was established. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene set enrichment analysis (GSEA), and gene-pathway correlation analysis were conducted using R software. To validate network pharmacology results, molecular docking, molecular dynamics simulations, and in vitro experiments were performed. RESULTS: We identified 599 Brevilin A-associated targets and 3864 lung cancer-related targets, with 155 overlapping genes considered as candidate targets for Brevilin A against lung cancer. The PPI network highlighted STAT3, TNF, HIF1A, PTEN, ESR1, and MTOR as potential therapeutic targets. GO and KEGG analyses revealed 2893 enriched GO terms and 157 enriched KEGG pathways, including the PI3K-Akt signaling pathway, FoxO signaling pathway, and HIF-1 signaling pathway. GSEA demonstrated a close association between hub genes and lung cancer. Gene-pathway correlation analysis indicated significant associations between hub genes and the cellular response to hypoxia pathway. Molecular docking and dynamics simulations confirmed Brevilin A's interaction with PTEN and HIF1A, respectively. In vitro experiments demonstrated Brevilin A-induced dose- and time-dependent cell death in A549 cells. Notably, Brevilin A treatment significantly reduced HIF-1α mRNA expression while increasing PTEN mRNA levels. CONCLUSIONS: This study demonstrates that Brevilin A exerts anti-cancer effects in treating lung cancer through a multi-target and multi-pathway manner, with the HIF pathway potentially being involved. These results lay a theoretical foundation for the prospective clinical application of Brevilin A.

Pancancer analysis of the prognostic and immunological role of FANCD2: a potential target for carcinogenesis and survival.

Recent evidence has shed light on the significant role of FANCD2 in cancer initiation, development, and progression. However, a comprehensive pan-cancer analysis of FANCD2 has been lacking. In this study, we have conducted a thorough investigation into the expression profiles and prognostic significance of FANCD2, as well as its correlation with clinicopathological parameters and immune cell infiltration, using advanced bioinformatic techniques. The results demonstrate that FANCD2 is significantly upregulated in various common cancers and is associated with prognosis. Notably, higher expression levels of FANCD2 are linked to poor overall survival, as indicated by Cox regression and Kaplan-Meier analyses. Additionally, we have observed a decrease in the methylation of FANCD2 DNA in some cancers, and this decrease is inversely correlated with FANCD2 expression. Genetic alterations in FANCD2 predominantly manifest as mutations, which are associated with overall survival, disease-specific survival, disease-free survival, and progression-free survival in certain tumor types. Moreover, FANCD2 exhibits a strong correlation with infiltrating cell levels, immune checkpoint genes, tumor mutation burden (TMB), and microsatellite instability (MSI). Enrichment analysis further highlights the potential impact of FANCD2 on Fanconi anemia (FA) pathway and cell cycle regulation. Through this comprehensive pan-cancer analysis, we have gained a deeper understanding of the functions of FANCD2 in oncogenesis and metastasis across different types of cancer.

Health warning based on 3R ECG Sample's combined features and LSTM.

Most researches use the fixed-length sample to identify ECG abnormalities based on MIT ECG dataset, which leads to information loss. To address this problem, this paper proposes a method for ECG abnormality detection and health warning based on ECG Holter of PHIA and 3R-TSH-L method. The 3R-TSH-L method is implemented by:(1) getting 3R ECG samples using Pan-Tompkins method and using volatility to obtain high-quality raw ECG data; (2) extracting combination features including time-domain features, frequency domain features and time-frequency domain features; (3) using LSTM for classification, training and testing the algorithm based on the MIT-BIH dataset, and obtaining relatively optimal features as spliced normalized fusion features including kurtosis, skewness and RR interval time domain features, STFT-based sub-band spectrum features, and harmonic ratio features. The ECG data were collected using the self-developed ECG Holter (PHIA) on 14 subjects, aged between 24 and 75 including both male and female, to build the ECG dataset (ECG-H). The algorithm was transferred to the ECG-H dataset, and a health warning assessment model based on abnormal ECG rate and heart rate variability weighting was proposed. Experiments show that 3R-TSH-L method proposed in the paper has a high accuracy of 98.28% for the detection of ECG abnormalities of MIT-BIH dataset and a good transfer learning ability of 95.66% accuracy for ECG-H. The health warning model was also testified to be reasonable. The key technique of the ECG Holter of PHIA and the method 3R-TSH-L proposed in this paper is expected to be widely used in family-oriented healthcare.

Learning to Generate Tips from Song Reviews.

Reviews of songs play an important role in online music service platforms. Prior research shows that users can make quicker and more informed decisions when presented with meaningful song reviews. However, reviews of songs are generally long in length and most of them are non-informative for users. It is difficult for users to efficiently grasp meaningful messages for making decisions. To solve this problem, one practical strategy is to provide tips, i.e., short, concise, empathetic, and self-contained descriptions about songs. Tips are produced from song reviews and should express non-trivial insights about the songs. To the best of our knowledge, no prior studies have explored the tip generation task in music domain. In this paper, we create a dataset named MTips for the task and propose a learning-to-generate framework named GenTMS for automatically generating tips from song reviews. The dataset involves 8,003 Chinese tips/non-tips from 128 songs which are distributed in five different song genres. Experimental results show that GenTMS achieves top-10 precision at 85.56%, outperforming the baseline models by at least 3.34%. Besides, to simulate the practical usage of our proposed framework, we also experiment with previously-unseen songs, during which GenTMS also achieves the best performance with top-10 precision at 78.89% on average. The results demonstrate the effectiveness of the proposed framework in tip generation of the music domain.

Centipeda minima active components and mechanisms in lung cancer.

BACKGROUND: Traditional Chinese medicine (TCM) has been extensively used for neoplasm treatment and has provided many promising therapeutic candidates. We previously found that Centipeda minima (C. minima), a Chinese medicinal herb, showed anti-cancer effects in lung cancer. However, the active components and underlying mechanisms remain unclear. In this study, we used network pharmacology to evaluate C. minima active compounds and molecular mechanisms in lung cancer. METHODS: We screened the TCMSP database for bioactive compounds and their corresponding potential targets. Lung cancer-associated targets were collected from Genecards, OMIM, and Drugbank databases. We then established a drug-ingredients-gene symbols-disease (D-I-G-D) network and a protein-protein interaction (PPI) network using Cytoscape software, and we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses using R software. To verify the network pharmacology results, we then performed survival analysis, molecular docking analysis, as well as in vitro and in vivo experiments. RESULTS: We identified a total of 21 C. minima bioactive compounds and 179 corresponding targets. We screened 804 targets related to lung cancer, 60 of which overlapped with C. minima. The top three candidate ingredients identified by D-I-G-D network analysis were quercetin, nobiletin, and beta-sitosterol. PPI network and core target analyses suggested that TP53, AKT1, and MYC are potential therapeutic targets. Moreover, molecular docking analysis confirmed that quercetin, nobiletin, and beta-sitosterol, combined well with TP53, AKT1, and MYC respectively. In vitro experiments verified that quercetin induced non-small cell lung cancer (NSCLC) cell death in a dose-dependent manner. GO and KEGG analyses found 1771 enriched GO terms and 144 enriched KEGG pathways, including a variety of cancer related pathways, the IL-17 signaling pathway, the platinum drug resistance pathway, and apoptosis pathways. Our in vivo experimental results confirmed that a C. minima ethanol extract (ECM) enhanced cisplatin (CDDP) induced cell apoptosis in NSCLC xenografts. CONCLUSIONS: This study revealed the key C. minima active ingredients and molecular mechanisms in the treatment of lung cancer, providing a molecular basis for further C. minima therapeutic investigation.

Label-Aware Distribution Calibration for Long-Tailed Classification.

Real-world data usually present long-tailed distributions. Training on imbalanced data tends to render neural networks perform well on head classes while much worse on tail classes. The severe sparseness of training instances for the tail classes is the main challenge, which results in biased distribution estimation during training. Plenty of efforts have been devoted to ameliorating the challenge, including data resampling and synthesizing new training instances for tail classes. However, no prior research has exploited the transferable knowledge from head classes to tail classes for calibrating the distribution of tail classes. In this article, we suppose that tail classes can be enriched by similar head classes and propose a novel distribution calibration (DC) approach named as label-aware DC (). transfers the statistics from relevant head classes to infer the distribution of tail classes. Sampling from calibrated distribution further facilitates rebalancing the classifier. Experiments on both image and text long-tailed datasets demonstrate that significantly outperforms existing methods. The visualization also shows that provides a more accurate distribution estimation.

Enriching query semantics for code search with reinforcement learning.

Code search is a common practice for developers during software implementation. The challenges of accurate code search mainly lie in the knowledge gap between source code and natural language (i.e., queries). Due to the limited code-query pairs and large code-description pairs available, the prior studies based on deep learning techniques focus on learning the semantic matching relation between source code and corresponding description texts for the task, and hypothesize that the semantic gap between descriptions and user queries is marginal. In this work, we found that the code search models trained on code-description pairs may not perform well on user queries, which indicates the semantic distance between queries and code descriptions. To mitigate the semantic distance for more effective code search, we propose QueCos, a Query-enriched Code search model. QueCos learns to generate semantic enriched queries to capture the key semantics of given queries with reinforcement learning (RL). With RL, the code search performance is considered as a reward for producing accurate semantic enriched queries. The enriched queries are finally employed for code search. Experiments on the benchmark datasets show that QueCos can significantly outperform the state-of-the-art code search models.

CRaDLe: Deep code retrieval based on semantic Dependency Learning.

Code retrieval is a common practice for programmers to reuse existing code snippets in the open-source repositories. Given a user query (i.e., a natural language description), code retrieval aims at searching the most relevant ones from a set of code snippets. The main challenge of effective code retrieval lies in mitigating the semantic gap between natural language descriptions and code snippets. With the ever-increasing amount of available open-source code, recent studies resort to neural networks to learn the semantic matching relationships between the two sources. The statement-level dependency information, which highlights the dependency relations among the program statements during the execution, reflects the structural importance of one statement in the code, which is favorable for accurately capturing the code semantics but has never been explored for the code retrieval task. In this paper, we propose CRaDLe, a novel approach for Code Retrieval based on statement-level semantic Dependency Learning. Specifically, CRaDLe distills code representations through fusing both the dependency and semantic information at the statement level, and then learns a unified vector representation for each code and description pair for modeling the matching relationship. Comprehensive experiments and analysis on real-world datasets show that the proposed approach can accurately retrieve code snippets for a given query and significantly outperform the state-of-the-art approaches on the task.

Clinical significance of fecal calprotectin for the early diagnosis of abdominal type of Henoch-Schonlein purpura in children.

The objective of this study is to explore the value of fecal calprotectin (FC) for early screening of the abdominal type of Henoch-Schonlein purpura (AHSP) in children. The study cohort included 40 children with AHSP treated at Shengjing Hospital of China Medical University from November 2014 to November 2015, and 40 children hospitalized in the Division of Pediatric Orthopedics in the corresponding period were selected as a control group. Fresh fecal samples were collected in the acute phase of the first visit (FC1), 3 days after treatment (FC2), and 7 days after treatment (FC3) from the AHSP group and the control group. Calprotectin levels in the fecal samples were measured using an enzyme-linked immunosorbent assay. At the same time, gastrointestinal performance and the laboratory examination indicators white blood cell (WBC) count and C-reactive protein (CRP) level were recorded. The median levels of FC1 (3053 µg/g) and FC2 (2778.3 µg/g) were higher than in the control group (102.5 µg/g), with significant differences among the three groups (p < 0.001). FC levels gradually decreased in remission, and the level of FC3 on day 7 was close to that of the control samples (p > 0.05). When the optimal cut-off was 264.5 µg/g, the area under the receiver operating characteristic (ROC) curve of FC for diagnosis of AHSP was 0.961 with a corresponding sensitivity and specificity of 93.1 and 87.5%, respectively. The levels of FC in children with AHSP were positively correlated with WBC count (r s = 0.688) and CRP value (r s = 0.513). The area under the ROC curve of WBC count for screening AHSP was 0.785 when the optimal cut-off value was 11.1 × 109/L with a corresponding sensitivity and specificity of 81.5 and 62.5%, respectively. The area under the ROC curve of CRP was 0.963 when the optimal cut-off value was 5.72 mg/dL with a corresponding sensitivity and specificity of 88.9 and 100%, respectively. Comparisons of FC, WBC count, and CRP level as diagnostic indicators of AHSP showed that the sensitivity of FC was higher than that of the WBC count and CRP level, and its diagnostic value was better than that of the WBC count. The levels of FC began to increase in the early stages of AHSP, showing a decreasing tendency in remission and tending to be within a normal range after a week or so. For the early diagnosis of AHSP, FC with a cut-off level of 264.5 µg/g has good sensitivity and specificity. The sensitivity of FC is better than that of the traditional inflammation indicators CRP and WBC count, and its diagnostic performance is better than WBC count; FC can be suitable as a new marker for the early diagnosis of AHSP.

[Effect of precursor on growth and accumulation of alkaloids of Lycoris radiata suspension cells].

In order to investigate the effects of phenylalanine, tyrosine and tyramine on the growth of Lycoris radiata suspension cells and the accumulation of alkaloids, the growth quantity of the cells as well as the content of alkaloids in cells were determined, which were treated with above three kinds of precursors alone and phenylalanine combined with tyrosine respectively. The results indicate that the addition of phenylalanine alone and addition of phenylalanine on the basis of tyrosine at high concentration (200 micromol/L) had no significant effect on the growth of Lycoris radiata suspension cells and the content of alkaloids in cells; whereas tyrosine and tyramine promoted the growth of the cells and alkaloids accumulation. Treated with tyrosine at high concentration (200 micromol/L), the content of alkaloids of the cells was 2.56-fold higher than that of the control group, the amounts of lycoramine (3.77 mg/g) and galanthamine (4.46 mg/g) were 6.61-fold and 6.97-fold higher than that of the control group, respectively. When treated with tyramine (200 micromol/L), the amount of alkaloids in Lycoris radiata suspension cells was 2.63-fold higher than that of the control group, and the amounts of lycoramine (4.45 mg/g) and galanthamine (5.14 mg/g) were 9.08-fold and 9.18-fold higher than that of the control group, respectively. The above results demonstrate that adding tyrosine and tyramine in the media significantly promoted the growth of the Lycoris radiata suspension cells and alkaloids accumulation in the cells.

[Determination of five endogenous hormones in wheat by high performance liquid chromatography].

A high performance liquid chromatographic (HPLC) method was developed to determine the five endogenous hormones including indole-3-acetic acid (IAA), abscisic acid (ABA), gibberellic acid (GA3 ), zeatin (ZT) and salicylic acid (SA) in wheat. The separation conditions were optimized, and methanol was chosen as the extraction solvent. Then the extract was extracted by petroleum ether and ethyl acetate, and purified with the Sep-Pak C18 column. The chromatographic conditions were as follows: Eclipse XDB-C18 reversed phase column (250 mm x 4.6 mm, 5 microm), the flow rate of 1 mL/min, the injection volume of 10 microL, and the detection wavelength of 240 nm were used for the separation of SA from 14.5 min to 18 min, while the detection at 254 nm used for the separation of the others. Methanol (A) and acetic acid aqueous solution (pH 3.6) (B) were used as the mobile phases with the linear gradient set as follows: 0-7 min 20% A, 7-10 min 20% A-28% A, 10-17 min 28% A, 17-19 min 28% A-40% A, 19-35 min 40% A. The results showed that: the hormones were separated well with the recoveries of 96.9% - 98%, and the RSDs were in the range of 1.54% to 2.29%. It is a reliable method for rapid, accurate separation and determination of the endogenous hormones in wheat.