The incidence of Alzheimer’s disease (AD) is increasing year by year, which brings great challenges to human health. However, the pathogenesis of AD is still unclear, and it lacks early diagnostic targets. The entorhinal cortex (EC) is a key brain region for the occurrence of AD neurodegeneration, and neuroinflammation plays a significant role in EC degeneration in AD. This study aimed to reveal the close relationship between inflammation-related genes in the EC and AD by detecting key differentially expressed genes (DEGs) via gene function enrichment pathway analysis. GSE4757 and GSE21779 gene expression profiles of AD were downloaded from the Gene Expression Omnibus (GEO) database. R language was used for the standardization and differential analysis of DEGs. Then, significantly enriched Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were analyzed to predict the potential biological functions of the DEGs. Finally, the significant expressions of identified DEGs were verified, and the therapeutic values were detected by a receiver operating characteristic (ROC) curve. The results showed that eight up-regulated genes (SLC22A2, ITGB2-AS1, NIT1, FGF14-AS2, SEMA3E, PYCARD, PRORY, ADIRF) and two down-regulated genes (AKAIN1, TRMT2B) may have a potential diagnostic value for AD, and participate in inflammatory pathways. The area under curve (AUC) results of the ten genes showed that they had potential diagnostic value for AD. The AUC of PYCARD was 0.95, which had the most significant diagnostic value, and it is involved in inflammatory processes such as the inflammasome complex adaptor protein. The DEGs screened, and subsequent pathway analysis revealed a close relationship between inflammation-related PYCARD and AD, thus providing a new basis for an early diagnostic target for AD.

Introduction. Klebsiella pneumoniae , a gram-negative bacterium, is a common pathogen causing nosocomial infection. The drug-resistance rate of K. pneumoniae is increasing year by year, posing a severe threat to public health worldwide. K. pneumoniae has been listed as one of the pathogens causing the global crisis of antimicrobial resistance in nosocomial infections. We need to explore the drug resistance of K. pneumoniae for clinical diagnosis. Single nucleotide polymorphisms (SNPs) are of high density and have rich genetic information in whole-genome sequencing (WGS), which can affect the structure or expression of proteins. SNPs can be used to explore mutation sites associated with bacterial resistance. Hypothesis/Gap Statement. Machine learning methods can detect genetic features associated with the drug resistance of K. pneumoniae from whole-genome SNP data. Aims. This work used Fast Feature Selection (FFS) and Codon Mutation Detection (CMD) machine learning methods to detect genetic features related to drug resistance of K. pneumoniae from whole-genome SNP data. Methods. WGS data on resistance of K. pneumoniae strains to four antibiotics (tetracycline, gentamicin, imipenem, amikacin) were downloaded from the European Nucleotide Archive (ENA). Sequence alignments were performed with MUMmer 3 to complete SNP calling using K. pneumoniae HS11286 chromosome as the reference genome. The FFS algorithm was applied to feature selection of the SNP dataset. The training set was constructed based on mutation sites with mutation frequency >0.995. Based on the original SNP training set, 70% of SNPs were randomly selected from each dataset as the test set to verify the accuracy of the training results. Finally, the resistance genes were obtained by the CMD algorithm and Venny. Results. The number of strains resistant to tetracycline, gentamicin, imipenem and amikacin was 931, 1048, 789 and 203, respectively. Machine learning algorithms were applied to the SNP training set and test set, and 28 and 23 resistance genes were predicted, respectively. The 28 resistance genes in the training set included 22 genes in the test set, which verified the accuracy of gene prediction. Among them, some genes (KPHS_35310, KPHS_18220, KPHS_35880, etc.) corresponded to known resistance genes (Eef2, lpxK, MdtC, etc). Logistic regression classifiers were established based on the identified SNPs in the training set. The area under the curves (AUCs) of the four antibiotics was 0.939, 0.950, 0.912 and 0.935, showing a strong ability to predict bacterial resistance. Conclusion. Machine learning methods can effectively be used to predict resistance genes and associated SNPs. The FFS and CMD algorithms have wide applicability. They can be used for the drug-resistance analysis of any microorganism with genomic variation and phenotypic data. This work lays a foundation for resistance research in clinical applications.

Gastric cancer is a common tumor with high morbidity and mortality. MicroRNA (miRNA) can regulate gene expression at the translation level and various tumorigenesis processes, playing an important role in tumor occurrence and prognosis. This study aims to screen miRNA associated with gastric cancer prognosis as biomarkers and explore the regulatory genes and related signaling pathways. In this work, R language was used for the standardization and differential analysis of miRNA and mRNA expression profiles. Samples were randomly divided into a testing group and a training group. Subsequently, we built the five miRNAs (has-miR-9-3p, has-miR-135b-3p, has-miR-143-5p, has-miR-942-3p, has-miR-196-3p) prognostic modules, verified and evaluated their prediction ability by the Cox regression analysis. They can be used as an independent factor in the prognosis of gastric cancer. By predicting and analyzing potential biological functions of the miRNA target genes, this study found that the AR gene was not only a hub gene in the PPI network, but also associated with excessive survival of patients. In conclusion, this study demonstrated that hsa-miR-942-3p could be a potential prognostic marker of gastric cancer associated with the AR and MAPK/ERK signaling pathways. The results of this study provide insights into the occurrence and development of gastric cancer.

Background: Alzheimer's disease (AD) is a common neurodegenerative disease, where neuroinflammation significantly influences its pathophysiology by driving the disease's pathological cascade. As a pro-inflammatory regulator, miR-125b-5p contributes to AD progression, though its precise role and mechanisms remain unclear. Objective: We aims to identify mRNAs significantly regulated by pro-inflammatory miR-125b-5p in AD and uncover key neuroinflammatory pathways. Methods: Target mRNAs regulated by miR-125b-5p were predicted using online databases and analyzed with two mRNA datasets to identify differentially expressed mRNAs (DEmRNAs). Enrichment analysis was conducted to explore their biological functions and pathways. The significance of DEmRNAs expression in AD-related inflammatory pathways was verified by the Wilcoxon test, predictive accuracy was assessed via area under the curves (AUCs), and novel mRNAs were identified through positive control analysis. Results: A total of 613 miR-125b-5p target mRNAs were identified, and 44 DEmRNAs were detected to be regulated by miR-125b-5p in two datasets. The 44 target DEmRNAs associated with AD include three key pathways: insulin signaling ( EXOC7, FLOT2, MKNK2), phosphatidylinositol signaling ( IP6K1, MTMR3), and phospholipase D signaling ( CYTH1, GAB2). Correlation analysis indicated strong correlations among 7 mRNAs, all showing significant differential expression, with AUCs above 0.5, confirming their predictive value. Three mRNAs ( EXOC7, IP6K1, CYTH1) were identified as novel AD-related genes. MiR-125b-5p binding sites in the 3′-UTRs of these 7 mRNAs suggest their potential roles in AD-related inflammation and signaling pathways. Conclusions: This study investigates the pro-inflammatory miR-125b-5p's role in the pathological processes of AD, highlighting its regulation of key target mRNAs and critical pathways.

With the frequent outbreak of viral pandemics, the search for efficient antiviral drugs has become an urgent task. Antiviral peptides (AVPs) have been proven to prevent the infection of host cells by viruses. This study proposes a novel tool called Datt-AVP for AVP prediction based on the peptide sequences. A dual-channel deep learning model of Long Short-Term Memory network and convolutional neural network, along with a pre-trained protein language model as a feature extractor, was applied to capture features in the antiviral peptide sequences simultaneously. Further-more, self-attention module was added to promote the performance of our prediction results. It showed good recognition ability for antiviral peptides with different lengths. Our tool achieved an accuracy rate of 96.1 on the benchmark dataset, which out-performed state-of-the-art tools. The source code of Datt-AVP can be freely available at https://github.com/niuwa2333/ Datt-AVP

Parkinson's disease (PD) represents a prevalent neurodegenerative disorder of the central nervous system, with an incidence that is on an upward trajectory. The death of dopaminergic neurons in the substantia nigra (SN) pars compacta and pathological changes are the main features of PD pathogenesis. Autophagy emerges as a pivotal cellular mechanism influencing neuronal attrition and the progression of PD. The main goal of this study was to clarify the close relationship between autophagy and the pathophysiology of PD. This study identified the differentially expressed autophagy genes (DEAGs) associated with PD in SN, which may contribute to the discovery of potential biomarkers for PD diagnosis. This study analyzed the GSE20291 and GSE42966 gene expression profiles from the gene expression omnibus (GEO) database. Analysis to identify differentially expressed genes (DEGs) was conducted utilizing the R programming language. From the database dedicated to autophagy-related genes, a compilation of 259 genes associated with autophagy was identified. The genes differentially expressed in PD and those related to autophagy were integrated and analyzed to identify DEAGs. Then, the differential genes of PD and autophagy genes were analyzed, and four genes (AKT2, ARSA, MAPK9, RGS19) were screened out. Finally, the potential value of DEAGs in PD pathology was verified by the receiver operating characteristic (ROC) curve. This study proposed the potential of autophagy-related genes as clinical biomarkers for PD, providing fresh perspectives on the pathogenic development of PD.