Database For Annotation Research Articles

Lycopene's therapeutic mechanisms in epididymitis: a network pharmacology and experimental study.

Epididymitis, a common disease of the male reproductive system, is often caused by nonspecific infections. Antibiotics alone cannot reverse histopathological changes or prevent long-term reproductive issues. Lycopene (LYC), a potent antioxidant, has shown potential in alleviating epididymitis, yet its specific mechanisms remain unclear. This study used network pharmacology and in vivo experiments to explore LYC's mechanisms in treating epididymitis. Epididymitis- and LYC-related target proteins were identified from multiple databases and analyzed using the Venny platform. Protein interactions were examined with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, and key targets were identified via topological analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using the Database for Annotation, Visualization and Integrated Discovery (DAVID) database. Target-pathway networks were visualized in Cytoscape, molecular docking was performed with AutoDock Vina, and LYC's effects were validated in a lipopolysaccharide (LPS)-induced epididymitis mouse model. Network pharmacology results indicated that LYC's effects involve the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) signaling pathway, which plays a crucial role in regulating inflammation and apoptosis. In vivo, LYC improved epididymal pathology, reduced inflammatory cell infiltration, and decreased key inflammatory cytokines, including interleukin-1 beta (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor alpha (TNF-α). By inhibiting PI3K/AKT activation, LYC modulated inflammation and reduced apoptosis. Additionally, LYC enhanced antioxidant enzyme activity and elevated the B-cell lymphoma-extra large (Bcl-xL) ratio, reducing oxidative stress and apoptosis. Molecular docking supported these findings, showing strong binding affinities with PI3K/AKT pathway targets. This study highlights LYC's potential as an adjunctive treatment for epididymitis, targeting inflammation and oxidative stress via the PI3K/AKT pathway. These findings suggest that LYC could enhance current therapies and provide new options for the clinical management of epididymitis.

Network pharmacology and molecular docking analysis on the mechanism of Wensan tincture in the treatment of pulmonary nodules: A review.

Network pharmacology and molecular docking methods were applied to elucidate the molecular mechanism of action of Wensan tincture (WST) in the treatment of pulmonary nodules. The Traditional Chinese Medicine Systems Pharmacology and the Traditional Chinese Medicine and Chemical Composition database were used to screen the active ingredients. Potential targets of WST were retrieved using Traditional Chinese Medicine Systems Pharmacology, SwissADME, and SwissTargetPrediction, while pulmonary nodule-associated targets were obtained from GeneCards and Online Mendelian Inheritance in Man databases. An active ingredient-target network was constructed using Cytoscape 3.9.1, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were conducted via the Database for Annotation, Visualization, and Integrated Discovery platform to identify core targets and signaling pathways. Molecular docking studies were performed using AutoDockTools. The results revealed 62 active ingredients and 344 corresponding targets within the tincture, alongside 1005 targets associated with pulmonary nodules. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses indicated that the potential therapeutic targets of WST include signal transducer and activator of transcription 3, mitogen-activated protein kinase-3, mitogen-activated protein kinase-1, Jun proto-oncogene, tumor protein 53, phosphoinositide-3-kinase regulatory subunit 1, heat shock protein 90 alpha family class A member 1, and AKT serine/threonine kinase 1. The primary pathways were the cancer pathway, mitogen-activated protein kinase signaling, advanced glycation end-products and their receptor signaling, epidermal growth factor receptor signaling, hypoxia-inducible factor-1 signaling, and the programmed cell death-ligand 1/programmed cell death protein 1 checkpoint pathways. Molecular docking demonstrated that quercetin exhibited the strongest binding affinity with mitogen-activated protein kinase-3, with a binding energy of -9.1 kcal/mol. Notably, key components of WST, such as quercetin, demonstrate considerable potential as drug candidates for the treatment of pulmonary nodules.

Network pharmacology‑based analysis and in vitro experimental verification of the inhibitory role of luteoloside on gastric cancer cells via the p53/p21 pathway.

The present study aimed to investigate the inhibitory effect of luteoloside on the proliferation, migration and invasion of gastric cancer (GC) cells based on network pharmacology and in vitro experiments. GC-associated targets were obtained from the GeneCards and Online Mendelian Inheritance in Man databases. Gene Ontology functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed using the Database for Annotation, Visualization and Integrated Discovery. Protein-protein interaction (PPI) networks and herb-active ingredient-target gene-signaling pathway networks were constructed using the Search Tool for the Retrieval of Interacting Genes and proteins and Cytoscape software to analyze core target genes and pathways. In addition, the alkaline comet assay was performed to assess DNA damage, demonstrating that luteoloside induces DNA double-strand breaks in a concentration-dependent manner, as indicated by increased comet tail lengths. γ-H2AX detection through western blot analysis further corroborated these findings, showing significant upregulation of this DNA damage marker in luteoloside-treated GC cells. The human GC cell line NCI-N87 was utilized for in vitro experiments to investigate the impact of different doses of luteoloside on cell proliferation, invasion and migration using Cell Counting Kit-8, scratch-wound and Transwell assays, respectively. The underlying molecular mechanism of luteoloside was explored using western blot analysis. The successfully constructed PPI network revealed the p53, Akt1, Bcl-2 and Caspase-3 proteins as the core targets, all of which showed good binding activity with luteoloside. The in vitro experiments demonstrated that luteoloside treatment significantly inhibited GC-cell proliferation, migration and invasion. The western blot results showed notable concentration-dependent upregulation of p53 and p21 protein expression and downregulation of Bcl-2 protein expression following luteoloside treatment. Overall, luteoloside inhibited the proliferation, migration and invasion of GC cells by activating the p53/p21 signaling pathway.

Chinese Clinical Named Entity Recognition With Segmentation Synonym Sentence Synthesis Mechanism: Algorithm Development and Validation.

Clinical named entity recognition (CNER) is a fundamental task in natural language processing used to extract named entities from electronic medical record texts. In recent years, with the continuous development of machine learning, deep learning models have replaced traditional machine learning and template-based methods, becoming widely applied in the CNER field. However, due to the complexity of clinical texts, the diversity and large quantity of named entity types, and the unclear boundaries between different entities, existing advanced methods rely to some extent on annotated databases and the scale of embedded dictionaries. This study aims to address the issues of data scarcity and labeling difficulties in CNER tasks by proposing a dataset augmentation algorithm based on proximity word calculation. We propose a Segmentation Synonym Sentence Synthesis (SSSS) algorithm based on neighboring vocabulary, which leverages existing public knowledge without the need for manual expansion of specialized domain dictionaries. Through lexical segmentation, the algorithm replaces new synonymous vocabulary by recombining from vast natural language data, achieving nearby expansion expressions of the dataset. We applied the SSSS algorithm to the Robustly Optimized Bidirectional Encoder Representations from Transformers Pretraining Approach (RoBERTa) + conditional random field (CRF) and RoBERTa + Bidirectional Long Short-Term Memory (BiLSTM) + CRF models and evaluated our models (SSSS + RoBERTa + CRF; SSSS + RoBERTa + BiLSTM + CRF) on the China Conference on Knowledge Graph and Semantic Computing (CCKS) 2017 and 2019 datasets. Our experiments demonstrated that the models SSSS + RoBERTa + CRF and SSSS + RoBERTa + BiLSTM + CRF achieved F1-scores of 91.30% and 91.35% on the CCKS-2017 dataset, respectively. They also achieved F1-scores of 83.21% and 83.01% on the CCKS-2019 dataset, respectively. The experimental results indicated that our proposed method successfully expanded the dataset and remarkably improved the performance of the model, effectively addressing the challenges of data acquisition, annotation difficulties, and insufficient model generalization performance.

Mechanism of Asperosaponin VI Related to EGFR/MMP9/AKT/PI3K Pathway in Treatment of Rheumtoid Arthritis.

To explore the mechanism of action of asperosaponin VI (AVI) in the treatment of rheumatoid arthritis (RA) and validate it in ex vivo experiments using network pharmacology and molecular docking methods. The predicted targets of AVI were obtained from PharmMaper, UniProt and SwissTarget Prediction platforms, the disease targets were collected from Online Mendelian Inheritance in Man, Therapeutic Target Database and GeneCards databases, the intersection targets of AVI and RA were obtained from Venny 2.1.0, and the protein-protein interaction (PPI) network was obtained from STRING database, which was analyzed by Cytoscape software and screened to obtain the core targets. Cytoscape software was used to analyze PPI network and screen the core targets. Based on the Database for Annotation, Visualization and Integrated Discovery database, Gene Ontology functional and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed, and Cytoscape software was used to construct the "Disease-Pathway-Target-Drug" network, which was finally verified by molecular docking and animal experiments. Network pharmacological studies showed that AVI was able to modulate 289 targets, with 102 targets for the potential treatment of RA, with the core pathway being the AKT/PI3K signaling pathway, and the core targets being the epidermal growth factor receptor (EGFR) and matrix metalloproteinase 9 (MMP9). Molecular docking results showed that AVI could produce strong binding with both of the 2 core targets. In vitro cellular experiments showed that AVI reduced nitric oxide, prostaglandin E2, tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), and IL-1 β levels (P<0.05) and inhibited cyclooxygenase-2, nitric oxide synthase, EGFR, MMP9, phosphorylated phosphoinositide 3-kinase (p-PI3K), and phosphorylated serine-threonine kinase (p-AKT) proteins (P<0.05). The results of in vivo studies showed that AVI improved RA score and foot swelling thickness and decreased TNF-α, IL-6, p-PI3K and p-AKT levels in RA rats (P<0.05). AVI exerts anti-inflammatory and anti-RA effects which might be related to the EGFR/MMP9/AKT/PI3K pathway.

DNAproDB: an updated database for the automated and interactive analysis of protein-DNA complexes.

DNAproDB (https://dnaprodb.usc.edu/) is a database, visualization tool, and processing pipeline for analyzing structural features of protein-DNA interactions. Here, we present a substantially updated version of the database through additional structural annotations, search, and user interface functionalities. The update expands the number of pre-analyzed protein-DNA structures, which are automatically updated weekly. The analysis pipeline identifies water-mediated hydrogen bonds that are incorporated into the visualizations of protein-DNA complexes. Tertiary structure-aware nucleotide layouts are now available. New file formats and external database annotations are supported. The website has been redesigned, and interacting with graphs and data is more intuitive. We also present a statistical analysis on the updated collection of structures revealing salient patterns in protein-DNA interactions.

Exploring the osteogenic effects of simiao wan through activation of the PI3K/AKT pathway in osteoblasts

Ethnopharmacological relevanceOsteoporosis (OP) is a degenerative bone disease commonly associated with reduced bone density and increased fracture risk. Aim of the studyThis study aimed to validate the therapeutic effects of Simiao wan (SMW) on OP and explore the underlying mechanism, particularly focusing on the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) signaling pathway. Materials and methodsThe chemical components of SMW were identified using UPLC-Q-TOF-MS/MS. The obtained compounds were then input into the TCMSP, TargetNet, and SwissTargetPrediction databases to predict potential targets. OP-related targets were collected from the GeneCards and DisGeNET databases, and intersecting targets were identified through a Venn diagram. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the intersecting targets using the Database for Annotation, Visualization and Integrated Discovery (DAVID). SMW extract was subsequently used to treat osteoblasts in vitro, and its toxicity on osteoblasts was assessed using Cell Counting Kit-8 (CCK-8) and lactate dehydrogenase (LDH) release assays. Osteoblast differentiation and activity were further evaluated using alizarin red staining, alkaline phosphatase staining, and Western blot analyses to validate the activation of network pharmacological signaling pathways. ResultsA total of 121 potential targets were identified for SMW in the treatment of OP, with AKT1 as the primary target. The PI3K/AKT pathway emerged as a key signaling pathway potentially involved in SMW's therapeutic effects o OP. Toxicity assessments showed no significant toxicity of SMW on osteoblasts. Additionally, SMW promoted osteoblast proliferation, alkaline phosphatase activity, calcium nodule deposition, and the expression of osteogenic markers (osteocalcin (OCN), runt-related transcription factor 2 (RunX2), and collagen I), and activated the PI3K/AKT signaling pathway. The PI3K/AKT pathway inhibitor LY294002 partially reversed the SMW-induced mineral deposition and expression of OCN, RunX2, and collagen I. ConclusionSMW demonstrated effective multi-target and multi-pathway therapeutic potential in the treatment of OP, with a significant impact on the PI3K/AKT signaling pathway.

Comparative performance of YOLOv8, YOLOv9, YOLOv10, YOLOv11 and Faster R-CNN models for detection of multiple weed species

Weeds pose a serious production challenge in various agronomic crops by reducing their grain yields. Increasing cases of herbicide-resistant (HR) weed populations further exacerbate the problem. Future weed control tactics require the integration of non-chemical and reduced chemical-based strategies that can target site- and specie-specific weed management (SSSWM). Advanced machine learning technology has the potential to localize and detect weed seedlings to implement SSSWM. However, due to large biological variability among various weed species and environmental conditions where they grow, accurate and precise weed detection remains challenging. The main objectives of this research were to (1) develop an annotated image database of cocklebur (Xanthium strumarium L.), dandelion (Taraxacum officinale), common waterhemp (Amaranthus tuberculatus), Palmer amaranth (Amaranthus palmeri) and common lambsquarters (Chenopodium album L.), and (2) investigate the comparative performance (speed and accuracy) of YOLOv8, YOLOv9, YOLOv10, YOLOv11 and Faster R-CNN algorithms in detecting those weed species. A weed dataset with bounding box annotations for each weed species was created, consisting of images collected under variable field conditions which were preprocessed and augmented to create a dataset of 2348 color images. The YOLOv8, YOLOv9, YOLOv10, YOLOv11 and Faster R-CNN were trained using the annotated weed image database to detect each weed species. Results indicated that YOLOv11 was the fastest model with inference time of 13.5 milliseconds (ms) followed by YOLOv8 and YOLOv10 with inference time 23 and 19.3 milliseconds (ms), respectively. The YOLOv9 had the highest accuracy in detecting different weed species with an overall mean average precision (mAP@0.5) of 0.935. In contrast, the Detectron2 with Fast R-CNN configuration provided mAP@0.5 of 0.821 with an inference time of 63.8 ms. These results suggest that the YOLO series algorithms have the potential for real-time deployment for weed species detection more accurately and faster than Faster R-CNN in agricultural fields.

Characterization of a Highly Virulent Klebsiella michiganensis Strain Isolated from a Preterm Infant with Sepsis.

Klebsiella michiganensis is an opportunistic pathogen that causes an increasing number of serious infections. This study aimed to investigate the etiology of the severe clinical symptoms of sepsis in preterm infants and the characterization of K. michiganensis isolates. Whole-genome sequencing (WGS) was performed on three strains isolated from an infected preterm infant. Additionally, the genomic sequences of 534 K. michiganensis strains were obtained from the NCBI database. To gain deeper insights into these strains, we utilized the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Clusters of Orthologous Groups (COG), and Pathogen Host Interactions (PHI) database annotation tools for comprehensive gene function analyses. Moreover, the multilocus sequence typing (MLST), EasyCGtree, and virulence factor database (VFDB) were employed to determine the sequence types (STs), construct phylogenetic trees, and identify potential virulence factors. Sequence analysis found that the three isolated strains had identical sequence characteristics and did not correspond to any of the known ST types. Virulence factor analysis revealed that the three strains harbored mrkABCDFHIJ, fimABCDEFGHIK, entABCDEFS, fepABCD, and capsule genes. These virulence factors are likely to play crucial roles in enhancing adhesion and metabolic capabilities, resisting phagocytosis (inducing immune cell damage), and ultimately contributing to prolonged bacteremia. The phylogenetic tree and comparative genomics of virulence factors showed the genetic and virulence factor diversity of the currently reported K. michiganensis strains. We identified a novel strain of K. michiganensis that exhibits high virulence and leads to severe septicemia phenotypes in preterm infants. Furthermore, comparative genomic analysis of previously reported K. michiganensis strains revealed the existence of three clades. This comprehensive analysis provides novel insights into the genetic relationships and virulence factor profiles of diverse strains of K. michiganensis. In future, it will be necessary to investigate the concept of the high virulence of K. michiganensis to determine the treatment method.

Research of decision-making artificial intelligence for oral implantation

The oral implant surgery robot could achieved basic "surgical operation intelligence"; however, "implant decision intelligence" has not yet been achieved. The author previously discussed the specific concept of implant decision artificial intelligence. During our exploration of implant decision intelligence, our team further integrated the clinical diagnosis and treatment process of oral implantation, along with the data characteristics of decision indicators and the distribution characteristics of demographic information. As a result, we identified five key scientific and technological issues in the process of oral implant decision intelligence, namely the construction of a specialized annotation database for oral implantation, the prediction of quantitative indicators, the application of three-dimensional imaging, the solution of data imbalance within indicators, and the joint output of multi-property and multimodal indicators in clinical pathways. This paper will review artificial intelligence research in oral implantology and our team's research progress, elaborating on the aforementioned challenges in intelligentization. It aims to provide references for addressing the mentioned scientific issues and guiding future research directions in the construction of implant decision intelligence in oral medicine.

Analysis of Straw Degradation and Whole Genome of Acrophialophora multiforma.

Lignin is one of the main components in plants, which can transform value-added bioenergy and chemicals. At the same time, due to the close combination of lignin and hemicellulose in the structure, it becomes a barrier for cellulose utilization. Therefore, the effective degradation of lignin is of great significance for the utilization of these resources. In this study, the lignin degrading ability of Acrophialophora multiforma strain GZUIFR 22.397 was preliminarily investigated through straw degradation experiments and enzyme activity determination. Then, the whole genome of strain A. multiforma GZUIFR 22.397 was sequentially analyzed and annotated through multiple gene function annotation databases to comprehensively evaluate its lignin degrading potential. The results showed that the weight loss of straw reached 5.98 ± 3.95%. Laccase activity was 77.49 ± 2.65 U/L, lignin peroxidase activity was 160.57 ± 29.07 U/L, and manganese peroxidase activity was 294.83 ± 3.77 U/L. The genome of A. multiforma strain GZUIFR 22.397 spans 33.81 megabases and encompasses 9,370 genes. Among these, 6,122 genes have been annotated in the Gene Ontology (GO), 2,286 in the Cluster of Orthologous Groups of proteins (KOG), 2,283 in the Kyoto Encyclopedia of Genes and Genomes (KEGG), and 603 in the Carbohydrate Active enZYmes Database (CAZy). Concurrently, the genome analysis predicted the presence of 9 genes for laccase, 2 genes for lignin peroxidase, and 2 genes for manganese peroxidase. In summary, these results indicated that A. multiforma GZUIFR 22.397 has lignin degrading ability, and laid the foundation for deciphering the molecular mechanism of A. multiforma GZUIFR 22.397 to degrade lignin.

Transcriptomic analysis of the gonads of Locusta migratoria (Orthoptera: Acrididae) following infection with Paranosema locustae.

Paranosema locustae is an environmentally friendly parasitic predator with promising applications in locust control. In this study, transcriptome sequencing was conducted on gonadal tissues of Locusta migratoria males and females infected and uninfected with P. locustae at different developmental stages. A total of 18,635 differentially expressed genes (DEGs) were identified in female ovary tissue transcriptomes, with the highest number of DEGs observed at 1 day post-eclosion (7141). In male testis tissue transcriptomes, a total of 32,954 DEGs were identified, with the highest number observed at 9 days post-eclosion (11,245). Venn analysis revealed 25 common DEGs among female groups and 205 common DEGs among male groups. Gene ontology and Kyoto Encyclopaedia of Genes and Genome analyses indicated that DEGs were mainly enriched in basic metabolism such as amino acid metabolism, carbohydrate metabolism, lipid metabolism, and immune response processes. Protein-protein interaction analysis results indicated that L. migratoria regulates the expression of immune- and reproductive-related genes to meet the body's demands in different developmental stages after P. locustae infection. Immune- and reproductive-related genes in L. migratoria gonadal tissue were screened based on database annotation information and relevant literature. Genes such as Tsf, Hex1, Apolp-III, Serpin, Defense, Hsp70, Hsp90, JHBP, JHE, JHEH1, JHAMT, and VgR play important roles in the balance between immune response and reproduction in gonadal tissues. For transcriptome validation, Tsf, Hex1, and ApoLp-III were selected and verified by quantitative real-time polymerase chain reaction (qRT-PCR). Correlation analysis revealed that the qRT-PCR expression patterns were consistent with the RNA-Seq results. These findings contribute to further understanding the interaction mechanisms between locusts and P. locustae.

“Idol talks!” AI-driven image to text to speech: illustrated by an application to images of deities

This work aims to provide an innovative solution to enhance the accessibility of images by an innovative image to text to speech system. It is applied to Hindu and Christian divine images. The method is applicable, among others, to enhance cultural understanding of these images by the visually impaired. The proposed system utilizes advanced object detection techniques like YOLO V5 and caption generation techniques like ensemble models. The system accurately identifies significant objects in images of Deities. These objects are then translated into descriptive and culturally relevant text through a Google text-to-speech synthesis module. The incorporation of text generation techniques from images introduces a new perspective to the proposed work. The aim is to provide a more comprehensive understanding of the visual content and allow visually impaired individuals to connect with the spiritual elements of deities through the immersive experience of auditory perception through a multimodal approach to make them feel inclusive in the community. This work is also applicable to preserve Cultural Heritage, Tourism and integrating with Virtual Reality (VR) and Augmented Reality (AR). Images of the artistic cultural legacy are hardly available in annotated databases, particularly those featuring idols. So we gathered, transcribed, and created a new database of Religious Idols in order to satisfy this requirement. In this paper, we experimented how to handle an issue of religious idol recognition using deep neural networks. In order to achieve this outcome, the network is first pre-trained on various deep learning models, and the best one which outperforms others is chosen. The proposed model achieves an accuracy of 96.75% for idol detection, and an approximate 97.06% accuracy for text generation according to the BLEU score.

Synergistic effect of reinforced cellulose nanofibrils/polyethylene glycol embedded particles in ammonia nitrogen wastewater: An in-depth microbial denitrification analysis

The encapsulation and immobilization technology provide a good growth environment for bacteria, strong protection ability, improved survival rate, and resistance to adverse environmental factors. Cellulose nanofibrils (CNF) with polyhydroxyl structure improve the elasticity, tensile properties, mechanical strength and gel strength of embedded particles through non-covalent forces. Polyethylene glycol (PEG) is a water-soluble polymer with unique carbon‑oxygen chain as the core skeleton. The ether bonds present in each unit give PEG extremely strong hydrophilicity and solubility. CNF and PEG to reinforce SA (sodium alginate) and PVA (polyvinyl alcohol)-SA embedded particle were adopted to treat ammonia nitrogen wastewater. The ammonia diffusion coefficients and oxygen diffusion coefficients of the CNF/PEG/SA and CNF/PEG/PVA-SA were 0.454 × 10−9, 0.286 × 10−9, 0.672 × 10−9 and 0.493 × 10−9 m2/s, respectively. The physicochemical properties of embedded particles were characterized by mass transfer characterization, SEM, XRD, FTIR, and BET analysis. The specific surface areas of CNF/PEG/SA and CNF/PEG/PVA-SA increased to 2.583 m2/g and 3.962 m2/g, respectively. The removal efficiency of NH4+-N, COD and TN in the CNF/PEG/PVA-SA system was 90 %, 74 % and 80 % at 30 °C. By HRT 8 h, the removal efficiency of NH4+-N, chemical oxygen demand (COD)and total nitrogen (TN) by the reinforced system was 94.35 %, 63.07 % and 73.84 %, respectively. The neutral to weakly alkaline pH range showed the highest removal efficiencies of NH4+-N, COD, and TN, at 88.51 %, 74.95 %, and 77.56 %, respectively. The half-saturation constant K was 82.98 mg/L, and the maximum ammonia oxidation rate (Vmax) was 358.92 mgN/(L-particles-h). The reinforced system showed zero-order reaction kinetics. Nonlinear fitting of each initial NH4+-N concentration and ammonia oxidation rate was carried out using the Michaelis-Menten equation. In reinforced embedded particles, microbial genomic data (KEGG; MetaCyc database annotation) analysis was conducted. The reinforced system demonstrated enhanced strength, specific surface area, mass transfer properties, resistance to adverse external environmental factors, and microbial survival rate for the effective treatment of NH4+-N wastewater.

Multi-Target Mechanisms of Si-Ni-San on Anxious Insomnia: An Example of Network-pharmacology and Molecular Docking Analysis.

Based on comprehensive network-pharmacology and molecular docking analysis, this study was intended to unveil the multiple mechanisms of Si-Ni-San (SNS) in treating anxious insomnia. The compounds of SNS were meticulously analyzed, selected and standardized with references to their pharmacological attributes. The components included chaihu (Bupleurum chinense DC.), baishao (Paeonia lactiflora Pall.), zhishi (Citrus aurantium L.) and gancao (Glycyrrhiza uralensis Fisch. ex DC.). We used the Traditional Chinese Medicine System Pharmacology (TCMSP) Database, Traditional Chinese Medicines Integrated Database (TCMID), GeneCards database, therapeutic target database (TTD) and comparative toxicogenomic database (CTD) to construct the components-compounds-targets networks and used Cytoscape 3.9.1 software to visualize the outcome. Afterwards, the STRING database and Cytoscape 3.9.1 software were utilized to construct and visualize the protein-protein interaction (PPI) network analysis. In addition, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were also conducted through the Database for Annotation, Visualization, and Integrated Discovery (DAVID). The molecular docking program was carried out using AutoDock 4.2 software to understand interactions between target receptors and compound ligands selected for study. We thoroughly sorted and filtered 31 pharmacologically active compounds from SNS. Subsequently, several potential target genes were predicted, of which there were 59 target genes distinctly associated with anxious insomnia. The PPI analysis indicated that the core target proteins included AKT1, IL6, TNF, SLC6A4, MAOA and GABRA2. The results of our study indicated that SNS potentially remediates anxious insomnia by reducing inflammation, neurodegeneration, and cell apoptosis of neurons. In addition, GO and KEGG enrichment analysis results indicated that SNS could modulate multiple aspects of anxious insomnia through mechanisms related to pathways of neuroactive ligand-receptor interaction. These pathways include various kinds of synaptic transmission pathways, and anti-inflammatory activity associated with response pathways. When we compared the components-compounds-targets networks and the compounds-targets-synaptic pathways networks, the five active compounds, including beta-Sitosterol, Kaempferol, Tetramethoxyluteolin, Isorhamnetin and Shinpterocarpin, were selected to conduct molecular docking experiments. Eleven target proteins, (AKT1, SLC6A4, ADRB2, MAOA, ACHE, ESR1, CYP3A4, CHRNA7, GABRA2, HTR2A and NOS3), which also play significant roles in regulating serotonergic, cholinergic, dopaminergic and GABAergic systems in the PPI network, were selected to act as receptors in molecular docking trials. The results showed that docking pairs isorhamnetin-AKT1, isorhamnetin-SLC6A4, β-sitosterol-MAOA, β- sitosterol-ACHE, isorhamnetin-CHRNA7 and shinpterocarpin-GABRA2 provided the most stable conformations of ligand-receptor binding between key compounds and core target proteins in the SNS. In the study, we offer a computational result, revealing that SNS may alleviate sleep disorders associated with anxiety through a "multi-compounds, multi-targets, and multi-pathways" mechanism. The network-pharmacology and molecular docking outcomes could theoretically confirm the anti-anxiety and anti-insomnia effects of SNS. Although this research is purely statistical and systematic without empirical validation, it serves as a stepping stone and cornerstone for subsequent experimental investigations.

Exploring the mechanisms of Cornus officinalis in the treatment of non-alcoholic steatohepatitis using network pharmacology, molecular docking and molecular dynamics simulations

Non-alcoholic steatohepatitis (NASH) is a predominant metabolic liver disease, typically characterized by hepatic steatosis, oxidative stress, and inflammation. The traditional Chinese medicine Cornus officinalis possesses anti-inflammatory and hepatoprotective pharmacological properties and has shown ameliorative effects on NASH. however, its mechanism of action remains unclear. This study aims to elucidate the mechanisms by which C. officinalis ameliorates NASH. The active components of C. officinalis were analyzed using the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP), and the corresponding targets were predicted. Subsequently, the DisGeNET, GeneCards, and GEO databases were employed to identify NASH-related targets. Venn diagrams were used to intersect the C. officinalis targets with the NASH targets. Protein–protein interaction (PPI) networks were constructed using the STRING database, and PPI network analysis was performed using Cytoscape. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using the Database for Annotation, Visualization, and Integrated Discovery (DAVID), followed by molecular docking validation. Cornus officinalis was found to contain 20 major active ingredients corresponding to 672 potential targets, 61 of which overlapped with NASH targets. PPI network, GO, and KEGG pathway analyses identified four targets with the highest correlation, and molecular docking results indicated that the active ingredients of C. officinalis exhibited strong binding affinities to NASH targets. The treatment of NASH with C. officinalis is characterized by multiple active ingredients and multiple targets, underscoring the major advantage of traditional Chinese medicine in treating NASH.

Exploring the protective effect and molecular mechanism of betulin in Alzheimer's disease based on network pharmacology, molecular docking and experimental validation.

Alzheimer's disease (AD) is a neurodegenerative disorder that impairs learning and memory, with high rates of mortality. Birch bark has been traditionally used in the treatment of various skin ailments. Betulin (BT) is a key compound of birch bark that exhibits diverse pharmacological benefits and therapeutic potential in AD. However, the therapeutic effects and molecular mechanisms of BT in AD remain unclear. The present study aimed to predict the potential therapeutic targets of BT in the treatment of AD, and to determine the specific underlying molecular mechanisms through network pharmacology analysis and experimental validation. PharmMapper was used to predict the target genes of BT, and four disease databases were searched to screen for AD targets. The intersection targets were identified using the jveen website. Drug‑disease target protein‑protein interaction networks and hub genes were obtained and visualized using the Search Tool for the Retrieval of Interacting Genes/Proteins database and Cytoscape. The Database for Annotation, Visualization and Integrated Discovery was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, and AutoDock was used for molecular docking analysis of BT and hub genes. Subsequently, the network‑predicted mechanisms of BT in AD were verified in vitro. A total of 495 BT and 1,386 AD targets were identified, and 120 were identified as potential targets of BT in the treatment of AD. The results of the molecular docking analysis revealed a strong binding affinity between BT and the hub genes. In addition, enrichment analyses of GO and KEGG pathways indicated that the neuroprotective effects of BT mainly involved the 'PI3K‑Akt signaling pathway'. The results of in vitro experiments demonstrated that pretreatment with BT for 2 h may ameliorate formaldehyde (FA)‑induced cytotoxicity and morphological changes in HT22 cells, and decrease FA‑induced Tau hyperphosphorylation and reactive oxygen species levels. Furthermore, the PI3K/AKT signaling pathway was activated and the expression levels of downstream proteins, namely GSK3β, Bcl‑2 and Bax, were modified following pre‑treatment with BT. Overall, the results of network pharmacology and in vitro analyses revealed that BT may reduce FA‑induced AD‑like pathology by modulating the PI3K/AKT signaling pathway, highlighting it as a potential multi‑target drug for the treatment of AD.

DAVID Ortholog: an integrative tool to enhance functional analysis through orthologs.

The Database for Annotation, Visualization, and Integrated Discovery (DAVID) is a web-based bioinformatics system for the functional interpretation of large lists of genes/proteins generated from high-throughput assays. It has been cited in 72 287 papers since its debut in 2003 as of 23 July 2024. The analysis is usually limited to the species of study. However, the knowledge of genes may be incomplete or unavailable for some species. Model organisms have been studied more extensively and analyzing gene lists in the context of these species can offer valuable insights, helping users better understand the genes and biological themes in their species of interest. We developed DAVID Ortholog for the conversion of gene lists between species. We utilized the ortholog data downloaded from Orthologous MAtrix (OMA) and Ensembl Compara as the base for the conversion. The OMA ortholog IDs and Ensembl gene IDs were converted to DAVID gene IDs and the pairing information of these IDs from these two sources was integrated into the DAVID Knowledgebase. DAVID Ortholog can convert the user's source gene list to an ortholog list of a desired species and the downstream DAVID analysis, in the context of that species, can be continued seamlessly, allowing users to further understand the biological meaning of their gene list based on the functional annotation found for the orthologs. https://davidbioinformatics.nih.gov/ortholog.jsp.

Identification and Validation of Telomere-Related Gene Signature in Intervertebral Disc Degeneration.

This study investigates the role of telomere-related differentially expressed genes (TRDEGs) in intervertebral disc degeneration (IVDD) through comprehensive bioinformatics analyses. Data were sourced from the Gene Expression Omnibus (GEO) with datasets GSE245147 and GSE124272 used for initial identification and validation, respectively. The GSE245147 dataset comprised transcriptional profiles from nucleus pulposus cells of both degenerated and non-degenerated human nucleus pulposus (NP) tissues. Using the limma package, 198TRDEGs were identified by intersecting differentially expressed genes (DEGs) with telomere-related genes (TRGs) from the TelNet database. Functional enrichment analyses using the Database for Annotation, Visualization and Integrated Discovery (DAVID) revealed that TRDEGs are significantly involved in cell division, chromosome segregation, and other mitotic processes. Protein-protein interaction (PPI) networks constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and visualized with Cytoscape (Cytoscape Consortium, San Diego, CA, USA) identified key hub genes such as CDK1, CCNA2, and AURKB. Pearson correlation and receiver operating characteristic (ROC) analyses highlighted five hub genes (ASPM, BUB1B, CDC20, KIF2C, TTK) with significant predictive value for IVDD. Additionally, mRNA-microRNA (miRNA) interaction analysis using NetworkAnalyst identified key miRNAs interacting with these hub genes. This study provides insights into the molecular mechanisms of IVDD and identifies potential targets for therapeutic intervention.

Artificial intelligence driven definition of food preference endotypes in UK Biobank volunteers is associated with distinctive health outcomes and blood based metabolomic and proteomic profiles

BackgroundSpecific food preferences can determine an individual’s dietary patterns and therefore, may be associated with certain health risks and benefits.MethodsUsing food preference questionnaire (FPQ) data from a subset comprising over 180,000 UK Biobank participants, we employed Latent Profile Analysis (LPA) approach to identify the main patterns or profiles among participants. blood biochemistry across groups/profiles was compared using the non-parametric Kruskal–Wallis test. We applied the Limma algorithm for differential abundance analysis on 168 metabolites and 2923 proteins, and utilized the Database for Annotation, Visualization and Integrated Discovery (DAVID) to identify enriched biological processes and pathways. Relative risks (RR) were calculated for chronic diseases and mental conditions per group, adjusting for sociodemographic factors.ResultsBased on their food preferences, three profiles were termed: the putative Health-conscious group (low preference for animal-based or sweet foods, and high preference for vegetables and fruits), the Omnivore group (high preference for all foods), and the putative Sweet-tooth group (high preference for sweet foods and sweetened beverages). The Health-conscious group exhibited lower risk of heart failure (RR = 0.86, 95%CI 0.79–0.93) and chronic kidney disease (RR = 0.69, 95%CI 0.65–0.74) compared to the two other groups. The Sweet-tooth group had greater risk of depression (RR = 1.27, 95%CI 1.21–1.34), diabetes (RR = 1.15, 95%CI 1.01–1.31), and stroke (RR = 1.22, 95%CI 1.15–1.31) compared to the other two groups. Cancer (overall) relative risk showed little difference across the Health-conscious, Omnivore, and Sweet-tooth groups with RR of 0.98 (95%CI 0.96–1.01), 1.00 (95%CI 0.98–1.03), and 1.01 (95%CI 0.98–1.04), respectively. The Health-conscious group was associated with lower levels of inflammatory biomarkers (e.g., C-reactive Protein) which are also known to be elevated in those with common metabolic diseases (e.g., cardiovascular disease). Other markers modulated in the Health-conscious group, ketone bodies, insulin-like growth factor-binding protein (IGFBP), and Growth Hormone 1 were more abundant, while leptin was less abundant. Further, the IGFBP pathway, which influences IGF1 activity, may be significantly enhanced by dietary choices.ConclusionsThese observations align with previous findings from studies focusing on weight loss interventions, which include a reduction in leptin levels. Overall, the Health-conscious group, with preference to healthier food options, has better health outcomes, compared to Sweet-tooth and Omnivore groups.Graphical