The impact of gestational weeks of Coronavirus disease 2019 (COVID-19) infection on perinatal outcomes.

BACKGROUND: To evaluate the relationship between coronavirus disease 2019 (COVID-19) infection at different time points during pregnancy and perinatal outcomes. METHODS: This retrospective study included 611 women who hospitalized for delivery between December 7 and April 30, 2023. Based on the different pregnancy weeks infected with COVID-19, the participants were divided into four groups: Group 1 (14-27+6 weeks gestation), Group 2 (28-36+6 weeks gestation), Group 3 (37-39+6 weeks gestation), and Group 4 (≥ 40 weeks gestation). Data including maternal demographic characteristics, clinical profiles, and perinatal outcomes were analyzed. RESULTS: There were no significant differences in maternal demographic characteristics among the four groups (P > 0.05). Compared to Groups 3 and 4, a higher rate of fever was noted in Groups 1 and 2 (P < 0.05). The frequency of preeclampsia and gestational diabetes mellitus showed a decreasing trend as pregnancy progressing (P < 0.05). Preterm delivery and neonatal intensive care unit admission were more frequently observed in Groups 1 and 2 than in Groups 3 and 4 (P < 0.05). Multivariate logistic regression analysis demonstrated that the timing of gestation in which COVID-19 was infected was not associated with preterm delivery and neonatal intensive care unit admission (P > 0.05), whereas gestational age at COVID-19 infection was negatively associated with the occurrence of preeclampsia and gestational diabetes mellitus (P < 0.05). CONCLUSIONS: Gestational age at COVID-19 infection is a simple parameter that predicts adverse perinatal outcomes to aid clinicians in determining to provide early enhanced prenatal care and increased monitoring to reduce maternal complications.

Images in abnormal placenta: a rare case of "blood bag" formation in placenta membranacea.

Placenta membranacea is an uncommon placental anomaly. Here, we present the case of a 30-year-old primiparous woman admitted for thickened placenta and reduced amniotic fluid. A follow-up ultrasound, performed after 48 h, revealed that the placental parenchyma was thin and not adequately visualized, enclosing a substantial volume of flowing blood (150 mm), with an amniotic fluid index of 18 mm. An emergency cesarean section was promptly performed. Following fetal delivery, a substantial accumulation of dark red blood within the fetal membranes created a "blood bag", estimated at approximately 3000 ml. This observation aligned with the ultrasound findings, and both placental morphology and pathological results substantiated the diagnosis of placenta membranacea.

The infection, cervical and perineal lacerations in relation to postpartum hemorrhage following vaginal delivery induced by Cook balloon catheter.

OBJECTIVE: To identify whether infection, cervical laceration and perineal laceration are associated with postpartum hemorrhage in the setting of vaginal delivery induced by Cook balloon catheter. MATERIALS AND METHODS: The retrospective study included 362 women who gave birth vaginally at or beyond 37 weeks of gestation with a diagnosis of postpartum hemorrhage between February 2021 to May 2022, of which including 216 women with induction of labor (Cook balloon catheter followed by oxytocin or oxytocin) and 146 women with spontaneous delivery. Risk factors for postpartum hemorrhage were collected and compared. RESULTS: 362 women were divided into three groups, group 1 with spontaneous delivery, group 2 with oxytocin, group 3 with Cook balloon catheter followed by oxytocin. There was no significant difference in incidence of infection within three groups (P > 0.05). The rate of cervical laceration and perineal laceration was significantly higher in group 3 compared with groups 2 and 1 (P < 0.05); Multivariate logistic regression analysis found that compared with group 1, either group 3 or group 2 was associated with increased risks of cervical laceration and perineal laceration (P < 0.05), and compared with group 2, group 3 was not associated with increased risks of cervical laceration and perineal laceration (P > 0.05). CONCLUSION: Infection, cervical laceration and perineal laceration are identified not to be independent risk factors for postpartum hemorrhage for women undergoing labor with Cook balloon catheter; Cervical laceration and perineal laceration increase the risk of postpartum hemorrhage in women with labor induction.

Advances in computational methods for identifying cancer driver genes.

Cancer driver genes (CDGs) are crucial in cancer prevention, diagnosis and treatment. This study employed computational methods for identifying CDGs, categorizing them into four groups. The major frameworks for each of these four categories were summarized. Additionally, we systematically gathered data from public databases and biological networks, and we elaborated on computational methods for identifying CDGs using the aforementioned databases. Further, we summarized the algorithms, mainly involving statistics and machine learning, used for identifying CDGs. Notably, the performances of nine typical identification methods for eight types of cancer were compared to analyze the applicability areas of these methods. Finally, we discussed the challenges and prospects associated with methods for identifying CDGs. The present study revealed that the network-based algorithms and machine learning-based methods demonstrated superior performance.

Detection of Association Features Based on Gene Eigenvalues and MRI Imaging Using Genetic Weighted Random Forest.

In the studies of Alzheimer's disease (AD), jointly analyzing imaging data and genetic data provides an effective method to explore the potential biomarkers of AD. AD can be separated into healthy controls (HC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI) and AD. In the meantime, identifying the important biomarkers of AD progression, and analyzing these biomarkers in AD provide valuable insights into understanding the mechanism of AD. In this paper, we present a novel data fusion method and a genetic weighted random forest method to mine important features. Specifically, we amplify the difference among AD, LMCI, EMCI and HC by introducing eigenvalues calculated from the gene p-value matrix for feature fusion. Furthermore, we construct the genetic weighted random forest using the resulting fused features. Genetic evolution is used to increase the diversity among decision trees and the decision trees generated are weighted by weights. After training, the genetic weighted random forest is analyzed further to detect the significant fused features. The validation experiments highlight the performance and generalization of our proposed model. We analyze the biological significance of the results and identify some significant genes (CSMD1, CDH13, PTPRD, MACROD2 and WWOX). Furthermore, the calcium signaling pathway, arrhythmogenic right ventricular cardiomyopathy and the glutamatergic synapse pathway were identified. The investigational findings demonstrate that our proposed model presents an accurate and efficient approach to identifying significant biomarkers in AD.

Risk factors and foetal growth restriction associated with expectant treatment of early-onset preeclampsia.

OBJECTIVE: To identify the factors affecting expectant management of early-onset preeclampsia, and evaluate the correlation between expectant treatment and foetal growth restriction. MATERIALS AND METHODS: The retrospective study included 72 women who were admitted for early-onset preeclampsia between February 2018 to April 2021. Data included maternal clinical parameters, demographic and maternal and neonatal outcomes, which were analysed for correlation. RESULTS: Multiple logistic regression analysis demonstrated that the time interval from the onset of 24-h proteinuria to termination of pregnancy showed a strong correlation with the expectant treatment; Univariate logistic analysis confirmed that there was no correlation between expectant treatment and foetal growth restriction. CONCLUSION: There was a negative correlation between the duration of 24-h proteinuria and the expectant treatment of patients with early-onset preeclampsia; Expectant treatment could not improve the development of foetal growth restriction in patients with early-onset preeclampsia.KEY MESSAGESThe duration of 24-h proteinuria affects the effectiveness of expectant management of early-onset preeclampsia.Expectant management can reduce adverse neonatal outcomes due to iatrogenic preterm delivery, but it cannot improve the occurrence of foetal growth restriction.

Clinical Application of Multi-Index Combined Risk Assessment in Early Pregnancy for Screening of Preeclampsia.

Objective: To explore the predictive value of single-index screening or multi-index combined screening for preeclampsia. Methods: From January 1, 2019, to December 31, 2021, pregnant women with a singleton pregnancy who had been regularly checked in each center since the first trimester (between 11 and 14 weeks of gestation) were retrieved from multiple participating centers. The risk calculation software LifeCycle 7.0 was used to calculate the risk values before 32 weeks, 34 weeks, and 37 weeks of gestation, and through a receiver operating characteristic (ROC) curve analysis, the predictive values of pregnancy-associated protein A (PAPP-A), the placental growth factor (PLGF), the mean arterial pressure (MAP), the uterine artery pulsatility index (UTPI), or a combined multi-index were calculated for preeclampsia. Results: Finally, 22 pregnant women developed preeclampsia, and the area under the ROC curve of the PAPP-A + PLGF + MAP + UTPI combined screening program was greater than that of other screening programs before 37 weeks of gestation (AUC = 0.975, 0.946, or 0.840 for <32 weeks, <34 weeks, or <37 weeks, respectively). At 32 weeks, the Youden index was at its maximum. Conclusion: PAPP-A + PLGF + MAP + UTPI combined screening is the optimal screening mode for preeclampsia screening before 37 weeks of gestation, and the combined prediction using multiple indicators in early pregnancy is more suitable for predicting the risk of early-onset preeclampsia.

Multi-Modal Neuroimaging Neural Network-Based Feature Detection for Diagnosis of Alzheimer's Disease.

Alzheimer's disease (AD) is a neurodegenerative brain disease, and it is challenging to mine features that distinguish AD and healthy control (HC) from multiple datasets. Brain network modeling technology in AD using single-modal images often lacks supplementary information regarding multi-source resolution and has poor spatiotemporal sensitivity. In this study, we proposed a novel multi-modal LassoNet framework with a neural network for AD-related feature detection and classification. Specifically, data including two modalities of resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) were adopted for predicting pathological brain areas related to AD. The results of 10 repeated experiments and validation experiments in three groups prove that our proposed framework outperforms well in classification performance, generalization, and reproducibility. Also, we found discriminative brain regions, such as Hippocampus, Frontal_Inf_Orb_L, Parietal_Sup_L, Putamen_L, Fusiform_R, etc. These discoveries provide a novel method for AD research, and the experimental study demonstrates that the framework will further improve our understanding of the mechanisms underlying the development of AD.

Feature Fusion and Detection in Alzheimer's Disease Using a Novel Genetic Multi-Kernel SVM Based on MRI Imaging and Gene Data.

Voxel-based morphometry provides an opportunity to study Alzheimer's disease (AD) at a subtle level. Therefore, identifying the important brain voxels that can classify AD, early mild cognitive impairment (EMCI) and healthy control (HC) and studying the role of these voxels in AD will be crucial to improve our understanding of the neurobiological mechanism of AD. Combining magnetic resonance imaging (MRI) imaging and gene information, we proposed a novel feature construction method and a novel genetic multi-kernel support vector machine (SVM) method to mine important features for AD detection. Specifically, to amplify the differences among AD, EMCI and HC groups, we used the eigenvalues of the top 24 Single Nucleotide Polymorphisms (SNPs) in a p-value matrix of 24 genes associated with AD for feature construction. Furthermore, a genetic multi-kernel SVM was established with the resulting features. The genetic algorithm was used to detect the optimal weights of 3 kernels and the multi-kernel SVM was used after training to explore the significant features. By analyzing the significance of the features, we identified some brain regions affected by AD, such as the right superior frontal gyrus, right inferior temporal gyrus and right superior temporal gyrus. The findings proved the good performance and generalization of the proposed model. Particularly, significant susceptibility genes associated with AD were identified, such as CSMD1, RBFOX1, PTPRD, CDH13 and WWOX. Some significant pathways were further explored, such as the calcium signaling pathway (corrected p-value = 1.35 × 10-6) and cell adhesion molecules (corrected p-value = 5.44 × 10-4). The findings offer new candidate abnormal brain features and demonstrate the contribution of these features to AD.

Interannual and seasonal variations of permafrost thaw depth on the Qinghai-Tibetan Plateau: A comparative study using long short-term memory, convolutional neural networks, and random forest.

An accurate estimation of thaw depth is critical to understanding permafrost changes due to climate warming on the Qinghai-Tibetan Plateau (QTP). However, previous studies mainly focused on the interannual changes of active layer thickness (ALT) across the QTP, and little is known about the changes in the seasonal thaw depth. Machine learning (ML) is a critical tool to accurately estimate the ALT of permafrost, but a direct comparison of ML with deep learning (DL) in ALT projection regarding the model performance is still lacking. Here, ML, namely random forest (RF), and DL algorithms like convolutional neural networks (CNN) and long short-term memory (LSTM) neural networks were compared to estimate the interannual changes of ALT and seasonal thaw depth on the QTP. Meteorological series, in-situ collected ALT observations, and geospatial information were used as predictors. The results show that both ML and DL methods are capable of estimating ALT and seasonal thaw depth in permafrost areas. The CNN and LSTM models developed using longer lagging times exhibit better performance in thaw depth prediction while the RF models are either mediocre or sometimes even worse as the lagging time increases. The results show that the ALT from 2003 to 2011 on the QTP exhibits an increasing trend, especially in the northern region. In addition, 68.8%, 88.7%, 52.5%, and 47.5% of the permafrost regions on the QTP have deepened seasonal thaw depth in spring, summer, autumn, and winter, respectively. The correlation between air temperature and permafrost thaw depth ranges from 0.65 to 1 with the time lag ranging from 1 to 32 days. This study shows that ML and DL can be effectively used in retrieving ALT and seasonal thaw depth of permafrost, and could present an efficient way to figure out the interannual and seasonal variations of permafrost conditions under climate warming.

A Triple-Network Dynamic Connection Study in Alzheimer's Disease.

Alzheimer's disease (AD) was associated with abnormal organization and function of large-scale brain networks. We applied group independent component analysis (Group ICA) to construct the triple-network consisting of the saliency network (SN), the central executive network (CEN), and the default mode network (DMN) in 25 AD, 60 mild cognitive impairment (MCI) and 60 cognitively normal (CN) subjects. To explore the dynamic functional network connectivity (dFNC), we investigated dynamic time-varying triple-network interactions in subjects using Group ICA analysis based on k-means clustering (GDA-k-means). The mean of brain state-specific network interaction indices (meanNII) in the three groups (AD, MCI, CN) showed significant differences by ANOVA analysis. To verify the robustness of the findings, a support vector machine (SVM) was taken meanNII, gender and age as features to classify. This method obtained accuracy values of 95, 94, and 77% when classifying AD vs. CN, AD vs. MCI, and MCI vs. CN, respectively. In our work, the findings demonstrated that the dynamic characteristics of functional interactions of the triple-networks contributed to studying the underlying pathophysiology of AD. It provided strong evidence for dysregulation of brain dynamics of AD.

Research on Voxel-Based Features Detection and Analysis of Alzheimer's Disease Using Random Survey Support Vector Machine.

Alzheimer's disease (AD) is a degenerative disease of the central nervous system characterized by memory and cognitive dysfunction, as well as abnormal changes in behavior and personality. The research focused on how machine learning classified AD became a recent hotspot. In this study, we proposed a novel voxel-based feature detection framework for AD. Specifically, using 649 voxel-based morphometry (VBM) methods obtained from MRI in Alzheimer's Disease Neuroimaging Initiative (ADNI), we proposed a feature detection method according to the Random Survey Support Vector Machines (RS-SVM) and combined the research process based on image-, gene-, and pathway-level analysis for AD prediction. Particularly, we constructed 136, 141, and 113 novel voxel-based features for EMCI (early mild cognitive impairment)-HC (healthy control), LMCI (late mild cognitive impairment)-HC, and AD-HC groups, respectively. We applied linear regression model, least absolute shrinkage and selection operator (Lasso), partial least squares (PLS), SVM, and RS-SVM five methods to test and compare the accuracy of these features in these three groups. The prediction accuracy of the AD-HC group using the RS-SVM method was higher than 90%. In addition, we performed functional analysis of the features to explain the biological significance. The experimental results using five machine learning indicate that the identified features are effective for AD and HC classification, the RS-SVM framework has the best classification accuracy, and our strategy can identify important brain regions for AD.

Research on Frequent Itemset Mining of Imaging Genetics GWAS in Alzheimer's Disease.

As an efficient method, genome-wide association study (GWAS) is used to identify the association between genetic variation and pathological phenotypes, and many significant genetic variations founded by GWAS are closely associated with human diseases. However, it is not enough to mine only a single marker effect variation on complex biological phenotypes. Mining highly correlated single nucleotide polymorphisms (SNP) is more meaningful for the study of Alzheimer's disease (AD). In this paper, we used two frequent pattern mining (FPM) framework, the FP-Growth and Eclat algorithms, to analyze the GWAS results of functional magnetic resonance imaging (fMRI) phenotypes. Moreover, we applied the definition of confidence to FP-Growth and Eclat to enhance the FPM framework. By calculating the conditional probability of identified SNPs, we obtained the corresponding association rules to provide support confidence between these important SNPs. The resulting SNPs showed close correlation with hippocampus, memory, and AD. The experimental results also demonstrate that our framework is effective in identifying SNPs and provide candidate SNPs for further research.

Role of SNPs in the Biogenesis of Mature miRNAs.

Single nucleotide polymorphisms (SNPs) play a significant role in microRNA (miRNA) generation, processing, and function and contribute to multiple phenotypes and diseases. Therefore, whole-genome analysis of how SNPs affect miRNA maturation mechanisms is important for precision medicine. The present study established an SNP-associated pre-miRNA (SNP-pre-miRNA) database, named miRSNPBase, and constructed SNP-pre-miRNA sequences. We also identified phenotypes and disease biomarker-associated isoform miRNA (isomiR) based on miRFind, which was developed in our previous study. We identified functional SNPs and isomiRs. We analyzed the biological characteristics of functional SNPs and isomiRs and studied their distribution in different ethnic groups using whole-genome analysis. Notably, we used individuals from Great Britain (GBR) as examples and identified isomiRs and isomiR-associated SNPs (iso-SNPs). We performed sequence alignments of isomiRs and miRNA sequencing data to verify the identified isomiRs and further revealed GBR ethnographic epigenetic dominant biomarkers. The SNP-pre-miRNA database consisted of 886 pre-miRNAs and 2640 SNPs. We analyzed the effects of SNP type, SNP location, and SNP-mediated free energy change during mature miRNA biogenesis and found that these factors were closely associated to mature miRNA biogenesis. Remarkably, 158 isomiRs were verified in the miRNA sequencing data for the 18 GBR samples. Our results indicated that SNPs affected the mature miRNA processing mechanism and contributed to the production of isomiRs. This mechanism may have important significance for epigenetic changes and diseases.

Hippocampal Subregion and Gene Detection in Alzheimer's Disease Based on Genetic Clustering Random Forest.

The distinguishable subregions that compose the hippocampus are differently involved in functions associated with Alzheimer's disease (AD). Thus, the identification of hippocampal subregions and genes that classify AD and healthy control (HC) groups with high accuracy is meaningful. In this study, by jointly analyzing the multimodal data, we propose a novel method to construct fusion features and a classification method based on the random forest for identifying the important features. Specifically, we construct the fusion features using the gene sequence and subregions correlation to reduce the diversity in same group. Moreover, samples and features are selected randomly to construct a random forest, and genetic algorithm and clustering evolutionary are used to amplify the difference in initial decision trees and evolve the trees. The features in resulting decision trees that reach the peak classification are the important "subregion gene pairs". The findings verify that our method outperforms well in classification performance and generalization. Particularly, we identified some significant subregions and genes, such as hippocampus amygdala transition area (HATA), fimbria, parasubiculum and genes included RYR3 and PRKCE. These discoveries provide some new candidate genes for AD and demonstrate the contribution of hippocampal subregions and genes to AD.

Effect of APOE ε4 on multimodal brain connectomic traits: a persistent homology study.

BACKGROUND: Although genetic risk factors and network-level neuroimaging abnormalities have shown effects on cognitive performance and brain atrophy in Alzheimer's disease (AD), little is understood about how apolipoprotein E (APOE) ε4 allele, the best-known genetic risk for AD, affect brain connectivity before the onset of symptomatic AD. This study aims to investigate APOE ε4 effects on brain connectivity from the perspective of multimodal connectome. RESULTS: Here, we propose a novel multimodal brain network modeling framework and a network quantification method based on persistent homology for identifying APOE ε4-related network differences. Specifically, we employ sparse representation to integrate multimodal brain network information derived from both the resting state functional magnetic resonance imaging (rs-fMRI) data and the diffusion-weighted magnetic resonance imaging (dw-MRI) data. Moreover, persistent homology is proposed to avoid the ad hoc selection of a specific regularization parameter and to capture valuable brain connectivity patterns from the topological perspective. The experimental results demonstrate that our method outperforms the competing methods, and reasonably yields connectomic patterns specific to APOE ε4 carriers and non-carriers. CONCLUSIONS: We have proposed a multimodal framework that integrates structural and functional connectivity information for constructing a fused brain network with greater discriminative power. Using persistent homology to extract topological features from the fused brain network, our method can effectively identify APOE ε4-related brain connectomic biomarkers.

Multivariate genome wide association and network analysis of subcortical imaging phenotypes in Alzheimer's disease.

BACKGROUND: Genome-wide association studies (GWAS) have identified many individual genes associated with brain imaging quantitative traits (QTs) in Alzheimer's disease (AD). However single marker level association discovery may not be able to address the underlying biological interactions with disease mechanism. RESULTS: In this paper, we used the MGAS (Multivariate Gene-based Association test by extended Simes procedure) tool to perform multivariate GWAS on eight AD-relevant subcortical imaging measures. We conducted multiple iPINBPA (integrative Protein-Interaction-Network-Based Pathway Analysis) network analyses on MGAS findings using protein-protein interaction (PPI) data, and identified five Consensus Modules (CMs) from the PPI network. Functional annotation and network analysis were performed on the identified CMs. The MGAS yielded significant hits within APOE, TOMM40 and APOC1 genes, which were known AD risk factors, as well as a few new genes such as LAMA1, XYLB, HSD17B7P2, and NPEPL1. The identified five CMs were enriched by biological processes related to disorders such as Alzheimer's disease, Legionellosis, Pertussis, and Serotonergic synapse. CONCLUSIONS: The statistical power of coupling MGAS with iPINBPA was higher than traditional GWAS method, and yielded new findings that were missed by GWAS. This study provides novel insights into the molecular mechanism of Alzheimer's Disease and will be of value to novel gene discovery and functional genomic studies.

Persistent Feature Analysis of Multimodal Brain Networks Using Generalized Fused Lasso for EMCI Identification.

Early Mild Cognitive Impairment (EMCI) involves very subtle changes in brain pathological process, and thus identification of EMCI can be challenging. By jointly analyzing cross-information among different neuroimaging data, an increased interest recently emerges in multimodal fusion to better understand clinical measurements with respect to both structural and functional connectivity. In this paper, we propose a novel multimodal brain network modeling method for EMCI identification. Specifically, we employ the structural connectivity based on diffusion tensor imaging (DTI), as a constraint, to guide the regression of BOLD time series from resting state functional magnetic resonance imaging (rs-fMRI). In addition, we introduce multiscale persistent homology features to avoid the uncertainty of regularization parameter selection. An empirical study on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database demonstrates that the proposed method effectively improves classification performance compared with several competing approaches, and reasonably yields connectivity patterns specific to different diagnostic groups.

Genome-wide Network-assisted Association and Enrichment Study of Amyloid Imaging Phenotype in Alzheimer's Disease.

BACKGROUND: The etiology of Alzheimer's disease remains poorly understood at the mechanistic level, and genome-wide network-based genetics have the potential to provide new insights into the disease mechanisms. OBJECTIVE: The study aimed to explore the collective effects of multiple genetic association signals on an AV-45 PET measure, which is a well-known Alzheimer's disease biomarker, by employing a network assisted strategy. METHODS: First, we took advantage of a dense module search algorithm to identify modules enriched by genetic association signals in a protein-protein interaction network. Next, we performed statistical evaluation to the modules identified by dense module search, including a normalization process to adjust the topological bias in the network, a replication test to ensure the modules were not found randomly , and a permutation test to evaluate unbiased associations between the modules and amyloid imaging phenotype. Finally, topological analysis, module similarity tests and functional enrichment analysis were performed for the identified modules. RESULTS: We identified 24 consensus modules enriched by robust genetic signals in a genome-wide association analysis. The results not only validated several previously reported AD genes (APOE, APP, TOMM40, DDAH1, PARK2, ATP5C1, PVRL2, ELAVL1, ACTN1 and NRF1), but also nominated a few novel genes (ABL1, ABLIM2) that have not been studied in Alzheimer's disease but have shown associations with other neurodegenerative diseases. CONCLUSION: The identified genes, consensus modules and enriched pathways may provide important clues to future research on the neurobiology of Alzheimer's disease and suggest potential therapeutic targets.

Modulation of transcriptional activity in brain lower grade glioma by alternative splicing.

Proteins that modify the activity of transcription factors (TFs) are often called modulators and play a vital role in gene transcriptional regulation. Alternative splicing is a critical step of gene processing, and differentially spliced isoforms may have different functions. Alternative splicing can modulate gene function by adding or removing certain protein domains and thereby influence the activity of a protein. The objective of this study is to investigate the role of alternative splicing in modulating the transcriptional regulation in brain lower grade glioma (LGG), especially transcription factor ELK1, which is closely related to various disorders, including Alzheimer's disease and Down syndrome. The results showed that changes in the exon inclusion ratio of proteins APP and STK16 are associated with changes in the expression correlation between ELK1 and its targets. In addition, the structural features of the two modulators are strongly associated with the pathological impact of exon inclusion. The results of our analysis suggest that alternatively spliced proteins have different functions in modifying transcription factors and can thereby induce the dysregulation of multiple genes.