Identification of platelet-related subtypes and diagnostic markers in pediatric Crohn's disease based on WGCNA and machine learning.

Background: The incidence of pediatric Crohn's disease (PCD) is increasing worldwide every year. The challenges in early diagnosis and treatment of PCD persist due to its inherent heterogeneity. This study's objective was to discover novel diagnostic markers and molecular subtypes aimed at enhancing the prognosis for patients suffering from PCD. Methods: Candidate genes were obtained from the GSE117993 dataset and the GSE93624 dataset by weighted gene co-expression network analysis (WGCNA) and differential analysis, followed by intersection with platelet-related genes. Based on this, diagnostic markers were screened by five machine learning algorithms. We constructed predictive models and molecular subtypes based on key markers. The models were evaluated using the GSE101794 dataset as the validation set, combined with receiver operating characteristic curves, decision curve analysis, clinical impact curves, and calibration curves. In addition, we performed pathway enrichment analysis and immune infiltration analysis for different molecular subtypes to assess their differences. Results: Through WGCNA and differential analysis, we successfully identified 44 candidate genes. Following this, employing five machine learning algorithms, we ultimately narrowed it down to five pivotal markers: GNA15, PIK3R3, PLEK, SERPINE1, and STAT1. Using these five key markers as a foundation, we developed a nomogram exhibiting exceptional performance. Furthermore, we distinguished two platelet-related subtypes of PCD through consensus clustering analysis. Subsequent analyses involving pathway enrichment and immune infiltration unveiled notable disparities in gene expression patterns, enrichment pathways, and immune infiltration landscapes between these subtypes. Conclusion: In this study, we have successfully identified five promising diagnostic markers and developed a robust nomogram with high predictive efficacy. Furthermore, the recognition of distinct PCD subtypes enhances our comprehension of potential pathogenic mechanisms and paves the way for future prospects in early diagnosis and personalized treatment.

Predicting potential microbe-disease associations based on auto-encoder and graph convolution network.

The increasing body of research has consistently demonstrated the intricate correlation between the human microbiome and human well-being. Microbes can impact the efficacy and toxicity of drugs through various pathways, as well as influence the occurrence and metastasis of tumors. In clinical practice, it is crucial to elucidate the association between microbes and diseases. Although traditional biological experiments accurately identify this association, they are time-consuming, expensive, and susceptible to experimental conditions. Consequently, conducting extensive biological experiments to screen potential microbe-disease associations becomes challenging. The computational methods can solve the above problems well, but the previous computational methods still have the problems of low utilization of node features and the prediction accuracy needs to be improved. To address this issue, we propose the DAEGCNDF model predicting potential associations between microbes and diseases. Our model calculates four similar features for each microbe and disease. These features are fused to obtain a comprehensive feature matrix representing microbes and diseases. Our model first uses the graph convolutional network module to extract low-rank features with graph information of microbes and diseases, and then uses a deep sparse Auto-Encoder to extract high-rank features of microbe-disease pairs, after which the low-rank and high-rank features are spliced to improve the utilization of node features. Finally, Deep Forest was used for microbe-disease potential relationship prediction. The experimental results show that combining low-rank and high-rank features helps to improve the model performance and Deep Forest has better classification performance than the baseline model.

[Construction and evaluation of a model for predicting ischemic stroke risk in patients with sudden sensorineural hearing loss].

Objective:To explore the related factors of sudden sensorineural hearing loss complicated with ischemic stroke, construct the risk prediction model, and verify the prediction effect of the model. Methods:A retrospective analysis was performed on 901 sudden sensorineural hearing loss patients hospitalized from January 2017 to December 2020, The patients were divided into the ischemic stroke group（100 cases） and the sudden deafness group（801 cases） according to whether they were complicated with ischemic stroke, The independent correlation factors of sudden deafness complicated with ischemic stroke were screened by univariate analysis and multivariate Logistic regression model, and the risk prediction model and internal verification were established. The original data were randomly divided into the modeling group（631 cases） and the validation group（270 cases） at a 7∶3 ratio. Hosmer-Lemeshow and receiver operating characteristic curve were used to test the goodness of fit and predictive effect of the model, and 270 patients were included again in the application research of the model and to test the prediction effect of the model. Results:The results of single factor analysis showed that age, NEUR, NC, NLR, PLR, TC, HDL-C, BUN, TC-HDL-C, TG/HDL-C, LDL-C/HDL-C, Hcy, FIB and cervical vascular plaque were related factors of sudden sensorineural hearing loss complicated with ischemic stroke（P<0.05）. Age（OR=2.816）, NEUR（OR=2.707）, Hcy（OR=88.833）, FIB（OR=1.389）, TC-HDL-C（OR=1.613）, cervical vascular plaque（OR=2.862） are the independent risk factors of SNHL complicated with ischemic stroke. These 6 factors are used to construct a prediction model. Hosmer-lemeshow test results, the area under the ROC curve of the modeling group was 0.846, P=0.555, Youden index was 0.564, sensitivity was 0.820, and specificity was 0.744. In the validation group, the area under ROC curve was 0.847, P=0.288, Youden index was 0.432, sensitivity was 0.783, and specificity was 0.649. Conclusion:The risk prediction model constructed in this study shows good prediction efficiency. which can provide references for the clinical screening of ischemic stroke risks in patients with sudden sensorineural hearing loss and early interventions in early stage.

Construction and characterization of immunoliposomes targeting fibroblast growth factor receptor 3.

Fibroblast growth factor receptor 3 (FGFR3) plays an important regulatory role in tumor cell proliferation and drug resistance. FGFR3 is often constitutively active in many tumors. To deliver drugs into tumor cells by targeting FGFR3 will be a promising and potential strategy for cancer therapy. In this study, a novel fusion protein, ScFv-Cys containing a single chain variable fragment (ScFv) and an additional C-terminal cysteine residue, was generated at a rate of 10 mg/L of bacterial culture and purified at 95% by Ni-NTA chromatography. Subsequently, the recombinant ScFv-Cys was coupled with malPEG2000-DSPE and incorporated into liposomes to generate the immunoliposomes. The results indicated that immunoliposomes can specifically deliver the fluorescent molecules, Dio into bladder cancer cells highly expressing FGFR3. In conclusion, we successfully generated FGFR3-specific immunoliposomes, and proved its targeting effect and delivering ability.

Overexpression of Populus trichocarpa Mitogen-Activated Protein Kinase Kinase4 Enhances Salt Tolerance in Tobacco.

Mitogen-activated protein kinase (MAPK) is one of the factors of cascade reactions affecting responses to signal pathway of environmental stimuli. Throughout the life of plants, MAPK family members participate in signal transduction pathways and regulate various intracellular physiological and metabolic reactions. To gain insights into regulatory function of MAPK kinase (MAPKK) in Populus trichocarpa under salt stress, we obtained full-length cDNA of PtMAPKK4 and analyzed different expression levels of PtMAPKK4 gene in leaves, stems, and root organs. The relationship between PtMAPKK4 and salt stress was studied by detecting expression characteristics of mRNA under 150 mM NaCl stress using real-time quantitative polymerase chain reaction. The results showed that expression of PtMAPKK4 increased under salt (NaCl) stress in leaves but initially reduced and then increased in roots. Thus, salt stress failed to induce PtMAPKK4 expression in stems. PtMAPKK4 possibly participates in regulation of plant growth and metabolism, thereby improving its salt tolerance. We used Saccharomyces cerevisiae strain INVScI to verify subcellular localization of PtMAPKK4 kinase. The yeast strains containing pYES2-PtMAPKK4-GFP plasmid expressed GFP fusion proteins under the induction of d-galactose, and the products were located in nucleus. These results were consistent with network prediction and confirmed location of PtMAPKK4 enzyme in the nucleus. We tested NaCl tolerance in transgenic tobacco lines overexpressing PtMAPKK4 under the control of 35S promoter at germination stage to detect salt tolerance function of PtMAPKK4. Compared withK326 (a wild-type tobacco), lines overexpressing PtMAPKK4 showed a certain degree of improvement in tolerance, germination, and growth. NaCl inhibited growth of overexpressed line and K326 at the seedling stage. However, statistical analysis showed longer root length, higher fresh weight, and lower MDA content in transgenic lines in comparison with that in K326.