Single-nucleus multiomics reveals the disrupted regulatory programs in three brain regions of sporadic early-onset Alzheimer's disease.

Sporadic early-onset Alzheimer's disease (sEOAD) represents a significant but less-studied subtype of Alzheimer's disease (AD). Here, we generated a single-nucleus multiome atlas derived from the postmortem prefrontal cortex, entorhinal cortex, and hippocampus of nine individuals with or without sEOAD. Comprehensive analyses were conducted to delineate cell type-specific transcriptomic changes and linked candidate cis- regulatory elements (cCREs) across brain regions. We prioritized seven conservative transcription factors in glial cells in multiple brain regions, including RFX4 in astrocytes and IKZF1 in microglia, which are implicated in regulating sEOAD-associated genes. Moreover, we identified the top 25 altered intercellular signaling between glial cells and neurons, highlighting their regulatory potential on gene expression in receiver cells. We reported 38 cCREs linked to sEOAD-associated genes overlapped with late-onset AD risk loci, and sEOAD cCREs enriched in neuropsychiatric disorder risk loci. This atlas helps dissect transcriptional and chromatin dynamics in sEOAD, providing a key resource for AD research.

Cell-specific regulation of the circadian clock by BMAL1 in the paraventricular nucleus: Implications for regulation of systemic biological rhythms.

Circadian rhythms are internal biological rhythms driving temporal tissue-specific, metabolic programs. Loss of the circadian transcription factor BMAL1 in the paraventricular nucleus (PVN) of the hypothalamus reveals its importance in metabolic rhythms, but its functions in individual PVN cells are poorly understood. Here, loss of BMAL1 in the PVN results in arrhythmicity of processes controlling energy balance and alters peripheral diurnal gene expression. BMAL1 chromatin immunoprecipitation sequencing (ChIP-seq) and single-nucleus RNA sequencing (snRNA-seq) reveal its temporal regulation of target genes, including oxytocin (OXT), and restoring circulating OXT peaks in BMAL1-PVN knockout (KO) mice rescues absent activity rhythms. While glutamatergic neurons undergo day/night changes in expression of genes involved in cell morphogenesis, astrocytes and oligodendrocytes show gene expression changes in cytoskeletal organization and oxidative phosphorylation. Collectively, our findings show diurnal gene regulation in neuronal and non-neuronal PVN cells and that BMAL1 contributes to diurnal OXT secretion, which is important for systemic diurnal rhythms.

Aging- and alcohol-associated spatial transcriptomic signature in mouse acute pancreatitis reveals heterogeneity of inflammation and potential pathogenic factors.

The rapidly aging population is consuming more alcohol, leading to increased alcohol-associated acute pancreatitis (AAP) with high mortality. However, the mechanisms remain undefined, and currently there are no effective therapies available. This study aims to elucidate aging- and alcohol-associated spatial transcriptomic signature by establishing an aging AAP mouse model and applying Visium spatial transcriptomics for understanding of the mechanisms in the context of the pancreatic tissue. Upon alcohol diet feeding and caerulein treatment, aging mice (18 months) developed significantly more severe AAP with 5.0-fold increase of injury score and 2.4-fold increase of amylase compared to young mice (3 months). Via Visium spatial transcriptomics, eight distinct tissue clusters were revealed from aggregated transcriptomes of aging and young AAP mice: five acinar, two stromal, and one islet, which were then merged into three clusters: acinar, stromal, and islet for the comparative analysis. Compared to young AAP mice, > 1300 differentially expressed genes (DEGs) and approximately 3000 differentially regulated pathways were identified in aging AAP mice. The top five DEGs upregulated in aging AAP mice include Mmp8, Ppbp, Serpina3m, Cxcl13, and Hamp with heterogeneous distributions among the clusters. Taken together, this study demonstrates spatial heterogeneity of inflammatory processes in aging AAP mice, offering novel insights into the mechanisms and potential drivers for AAP development. KEY MESSAGES: Mechanisms regarding high mortality of AAP in aging remain undefined. An aging AAP mouse model was developed recapturing clinical exhibition in humans. Spatial transcriptomics identified contrasted DEGs in aging vs. young AAP mice. Top five DEGs were Mmp8, Ppbp, Serpina3m, Cxcl13, and Hamp in aging vs. young AAP mice. Our findings shed insights for identification of molecular drivers in aging AAP.

Systematic Transcriptome Profiling of hPSC-derived Osteoblasts Unveils CORIN's Mastery in Governing Osteogenesis Through CEBPD Modulation.

The commitment of stem cells to differentiate into osteoblasts is a highly regulated and complex process that involves the coordination of extrinsic signals and intrinsic transcriptional machinery. While rodent osteoblastic differentiation has been extensively studied, research on human osteogenesis has been limited by cell sources and existing models. Here, we systematically dissect hPSC-derived osteoblasts to identify functional membrane proteins and their downstream transcriptional networks involved in human osteogenesis. Our results reveal an enrichment of type II transmembrane serine protease CORIN in humans but not rodent osteoblasts. Functional analyses demonstrated that CORIN depletion significantly impairs osteogenesis. Genome-wide ChIP enrichment and mechanistic studies show that p38 MAPK-mediated CEBPD upregulation is required for CORIN-modulated osteogenesis. Contrastingly, the type I transmembrane heparan sulfate proteoglycan SDC1 enriched in MSCs exerts a negative regulatory effect on osteogenesis through a similar mechanism. ChIP-seq, bulk and single-cell transcriptomes, and functional validations indicated that CEBPD plays a critical role in controlling osteogenesis. In summary, our findings uncover previously unrecognized CORIN-mediated CEBPD transcriptomic networks in driving human osteoblast lineage commitment.

Improved liver disease prediction from clinical data through an evaluation of ensemble learning approaches.

PURPOSE: Liver disease causes two million deaths annually, accounting for 4% of all deaths globally. Prediction or early detection of the disease via machine learning algorithms on large clinical data have become promising and potentially powerful, but such methods often have some limitations due to the complexity of the data. In this regard, ensemble learning has shown promising results. There is an urgent need to evaluate different algorithms and then suggest a robust ensemble algorithm in liver disease prediction. METHOD: Three ensemble approaches with nine algorithms are evaluated on a large dataset of liver patients comprising 30,691 samples with 11 features. Various preprocessing procedures are utilized to feed the proposed model with better quality data, in addition to the appropriate tuning of hyperparameters and selection of features. RESULTS: The models' performances with each algorithm are extensively evaluated with several positive and negative performance metrics along with runtime. Gradient boosting is found to have the overall best performance with 98.80% accuracy and 98.50% precision, recall and F1-score for each. CONCLUSIONS: The proposed model with gradient boosting bettered in most metrics compared with several recent similar works, suggesting its efficacy in predicting liver disease. It can be further applied to predict other diseases with the commonality of predicate indicators.

Sex differences in metabolic adaptation in infants with cyanotic congenital heart disease.

BACKGROUND: Female infants with congenital heart disease (CHD) face significantly higher postoperative mortality rates after adjusting for cardiac complexity. Sex differences in metabolic adaptation to cardiac stressors may be an early contributor to cardiac dysfunction. In adult diseases, hypoxic/ischemic cardiomyocytes undergo a cardioprotective metabolic shift from oxidative phosphorylation to glycolysis which appears to be regulated in a sexually dimorphic manner. We hypothesize sex differences in cardiac metabolism are present in cyanotic CHD and detectable as early as the infant period. METHODS: RNA sequencing was performed on blood samples (cyanotic CHD cases, n = 11; controls, n = 11) and analyzed using gene set enrichment analysis (GSEA). Global plasma metabolite profiling (UPLC-MS/MS) was performed using a larger representative cohort (cyanotic CHD, n = 27; non-cyanotic CHD, n = 11; unaffected controls, n = 12). RESULTS: Hallmark gene sets in glycolysis, fatty acid metabolism, and oxidative phosphorylation were significantly enriched in cyanotic CHD females compared to male counterparts, which was consistent with metabolomic differences between sexes. Minimal sex differences in metabolic pathways were observed in normoxic patients (both controls and non-cyanotic CHD cases). CONCLUSION: These observations suggest underlying differences in metabolic adaptation to chronic hypoxia between males and females with cyanotic CHD. IMPACT: Children with cyanotic CHD exhibit sex differences in utilization of glycolysis vs. fatty acid oxidation pathways to meet the high-energy demands of the heart in the neonatal period. Transcriptomic and metabolomic results suggest that under hypoxic conditions, males and females undergo metabolic shifts that are sexually dimorphic. These sex differences were not observed in neonates in normoxic conditions (i.e., non-cyanotic CHD and unaffected controls). The involved metabolic pathways are similar to those observed in advanced heart failure, suggesting metabolic adaptations beginning in the neonatal period may contribute to sex differences in infant survival.

An improved method for diagnosis of Parkinson's disease using deep learning models enhanced with metaheuristic algorithm.

Parkinson's disease (PD) is challenging for clinicians to accurately diagnose in the early stages. Quantitative measures of brain health can be obtained safely and non-invasively using medical imaging techniques like magnetic resonance imaging (MRI) and single photon emission computed tomography (SPECT). For accurate diagnosis of PD, powerful machine learning and deep learning models as well as the effectiveness of medical imaging tools for assessing neurological health are required. This study proposes four deep learning models with a hybrid model for the early detection of PD. For the simulation study, two standard datasets are chosen. Further to improve the performance of the models, grey wolf optimization (GWO) is used to automatically fine-tune the hyperparameters of the models. The GWO-VGG16, GWO-DenseNet, GWO-DenseNet + LSTM, GWO-InceptionV3 and GWO-VGG16 + InceptionV3 are applied to the T1,T2-weighted and SPECT DaTscan datasets. All the models performed well and obtained near or above 99% accuracy. The highest accuracy of 99.94% and AUC of 99.99% is achieved by the hybrid model (GWO-VGG16 + InceptionV3) for T1,T2-weighted dataset and 100% accuracy and 99.92% AUC is recorded for GWO-VGG16 + InceptionV3 models using SPECT DaTscan dataset.

Targeting IGF2-IGF1R Signaling to Reprogram the Tumor Microenvironment for Enhanced Viro-Immunotherapy.

BACKGROUND: The FDA approval of oncolytic herpes simplex-1 virus (oHSV) therapy underscores its therapeutic promise and safety as a cancer immunotherapy. Despite this promise, the current efficacy of oHSV is significantly limited to a small subset of patients largely due to the resistance in tumor and tumor microenvironment (TME). METHODS: RNA sequencing (RNA-Seq) was used to identify molecular targets of oHSV resistance. Intracranial human and murine glioma or breast cancer brain metastasis (BCBM) tumor-bearing mouse models were employed to elucidate the mechanism underlying oHSV therapy-induced resistance. RESULTS: Transcriptome analysis identified IGF2 as one of the top secreted proteins following oHSV treatment. Moreover, IGF2 expression was significantly upregulated in 10 out of 14 recurrent GBM patients after treatment with oHSV, rQNestin34.5v.2 (71.4%) (p=0.0020) (ClinicalTrials.gov, NCT03152318). Depletion of IGF2 substantially enhanced oHSV-mediated tumor cell killing in vitro and improved survival of mice bearing BCBM tumors in vivo. To mitigate the oHSV-induced IGF2 in the TME, we constructed a novel oHSV, oHSV-D11mt, secreting a modified IGF2R domain 11 (IGF2RD11mt) that acts as IGF2 decoy receptor. Selective blocking of IGF2 by IGF2RD11mt significantly increased cytotoxicity, reduced oHSV-induced neutrophils/PMN-MDSCs infiltration, and reduced secretion of immune suppressive/proangiogenic cytokines, while increased CD8+cytotoxic T lymphocytes (CTLs) infiltration, leading to enhanced survival in GBM or BCBM tumor-bearing mice. CONCLUSION: This is the first study reporting that oHSV-induced secreted IGF2 exerts a critical role in resistance to oHSV therapy, which can be overcome by oHSV-D11mt as a promising therapeutic advance for enhanced viro-immunotherapy.

Mechanical-Enhanced and Durable Zwitterionic Hydrogel Coating for Inhibiting Coagulation and Reducing Bacterial Infection.

Blood-contact medical devices are indispensable for clinical interventions, yet their susceptibility to thrombosis and bacterial infections poses substantial risks to treatment efficacy and patient well-being. This study introduces a polysulfobetaine/alginate-CuII (SAC) zwitterionic hydrogel coating on polyurethane (PU) surfaces. This approach retains the superhydrophilic and antifouling nature of pSBMA while conferring the antibacterial effects of copper ions. Meanwhile, the copper alginate network intertwines with the polysulfobetaine (pSBMA) network, enhancing its mechanical properties and overcoming inherent weaknesses, thereby improving coating durability. Compared to the substrate, the SAC hydrogel coating significantly reduces thrombus adhesion mass by approximately 81.5% during extracorporeal blood circulation and effectively prevents bacterial biofilm formation even in a high-concentration bacterial milieu over 30 days. Moreover, the results from an isolated blood circulation model in New Zealand white rabbits affirm the impressive anticoagulant efficacy of the SAC hydrogel coating. The findings suggest that this hydrogel coating and its application method hold promise as a solution for blood-contact material surface modification to address thrombosis and bacterial biofilm formation simultaneously.

Dissecting the shared genetic architecture between Alzheimer's disease and frailty: a cross-trait meta-analyses of genome-wide association studies.

Introduction: Frailty is the most common medical condition affecting the aging population, and its prevalence increases in the population aged 65 or more. Frailty is commonly diagnosed using the frailty index (FI) or frailty phenotype (FP) assessments. Observational studies have indicated the association of frailty with Alzheimer's disease (AD). However, the shared genetic and biological mechanism of these comorbidity has not been studied. Methods: To assess the genetic relationship between AD and frailty, we examined it at single nucleotide polymorphism (SNP), gene, and pathway levels. Results: Overall, 16 genome-wide significant loci (15 unique loci) (p meta-analysis < 5 × 10-8) and 22 genes (21 unique genes) were identified between AD and frailty using cross-trait meta-analysis. The 8 shared loci implicated 11 genes: CLRN1-AS1, CRHR1, FERMT2, GRK4, LINC01929, LRFN2, MADD, RP11-368P15.1, RP11-166N6.2, RNA5SP459, and ZNF652 between AD and FI, and 8 shared loci between AD and FFS implicated 11 genes: AFF3, C1QTNF4, CLEC16A, FAM180B, FBXL19, GRK4, LINC01104, MAD1L1, RGS12, ZDHHC5, and ZNF521. The loci 4p16.3 (GRK4) was identified in both meta-analyses. The colocalization analysis supported the results of our meta-analysis in these loci. The gene-based analysis revealed 80 genes between AD and frailty, and 4 genes were initially identified in our meta-analyses: C1QTNF4, CRHR1, MAD1L1, and RGS12. The pathway analysis showed enrichment for lipoprotein particle plasma, amyloid fibril formation, protein kinase regulator, and tau protein binding. Conclusion: Overall, our results provide new insights into the genetics of AD and frailty, suggesting the existence of non-causal shared genetic mechanisms between these conditions.

VER-Net: a hybrid transfer learning model for lung cancer detection using CT scan images.

BACKGROUND: Lung cancer is the second most common cancer worldwide, with over two million new cases per year. Early identification would allow healthcare practitioners to handle it more effectively. The advancement of computer-aided detection systems significantly impacted clinical analysis and decision-making on human disease. Towards this, machine learning and deep learning techniques are successfully being applied. Due to several advantages, transfer learning has become popular for disease detection based on image data. METHODS: In this work, we build a novel transfer learning model (VER-Net) by stacking three different transfer learning models to detect lung cancer using lung CT scan images. The model is trained to map the CT scan images with four lung cancer classes. Various measures, such as image preprocessing, data augmentation, and hyperparameter tuning, are taken to improve the efficacy of VER-Net. All the models are trained and evaluated using multiclass classifications chest CT images. RESULTS: The experimental results confirm that VER-Net outperformed the other eight transfer learning models compared with. VER-Net scored 91%, 92%, 91%, and 91.3% when tested for accuracy, precision, recall, and F1-score, respectively. Compared to the state-of-the-art, VER-Net has better accuracy. CONCLUSION: VER-Net is not only effectively used for lung cancer detection but may also be useful for other diseases for which CT scan images are available.

Investigating gene functions and single-cell expression profiles of de novo variants in orofacial clefts.

Orofacial clefts (OFCs) are common congenital birth defects with various etiologies, including genetic variants. Online Mendelian Inheritance in Man (OMIM) annotated several hundred genes involving OFCs. Furthermore, several hundreds of de novo variants (DNVs) have been identified from individuals with OFCs. Some DNVs are related to known OFC genes or pathways, but there are still many DNVs whose relevance to OFC development is unknown. To explore novel gene functions and their cellular expression profiles, we focused on DNVs in genes that were not listed in OMIM. We collected 960 DNVs in 853 genes from published studies and curated these genes, based on the DNVs' deleteriousness, into 230 and 23 genes related to cleft lip with or without cleft palate (CL/P) and cleft palate only (CPO), respectively. For comparison, we curated 178 CL/P and 277 CPO genes from OMIM. In CL/P, the pathways enriched in DNV and OMIM genes were significantly overlapped (p = 0.002). Single-cell RNA sequencing (scRNA-seq) analysis of mouse lip development revealed that both gene sets had abundant expression in the ectoderm (DNV genes: adjusted p = 0.032, OMIM genes: adjusted p < 0.0002), while only DNV genes were enriched in the endothelium (adjusted p = 0.032). Although we did not achieve significant findings using CPO gene sets, which was mainly due to the limited number of DNV genes, scRNA-seq analysis implicated various expression patterns among DNV and OMIM genes. Our results suggest that combinatory pathway and scRNA-seq data analyses are helpful for contextualizing genes in OFC development.

DeepFace: Deep-learning-based framework to contextualize orofacial-cleft-related variants during human embryonic craniofacial development.

Orofacial clefts (OFCs) are among the most common human congenital birth defects. Previous multiethnic studies have identified dozens of associated loci for both cleft lip with or without cleft palate (CL/P) and cleft palate alone (CP). Although several nearby genes have been highlighted, the "casual" variants are largely unknown. Here, we developed DeepFace, a convolutional neural network model, to assess the functional impact of variants by SNP activity difference (SAD) scores. The DeepFace model is trained with 204 epigenomic assays from crucial human embryonic craniofacial developmental stages of post-conception week (pcw) 4 to pcw 10. The Pearson correlation coefficient between the predicted and actual values for 12 epigenetic features achieved a median range of 0.50-0.83. Specifically, our model revealed that SNPs significantly associated with OFCs tended to exhibit higher SAD scores across various variant categories compared to less related groups, indicating a context-specific impact of OFC-related SNPs. Notably, we identified six SNPs with a significant linear relationship to SAD scores throughout developmental progression, suggesting that these SNPs could play a temporal regulatory role. Furthermore, our cell-type specificity analysis pinpointed the trophoblast cell as having the highest enrichment of risk signals associated with OFCs. Overall, DeepFace can harness distal regulatory signals from extensive epigenomic assays, offering new perspectives for prioritizing OFC variants using contextualized functional genomic features. We expect DeepFace to be instrumental in accessing and predicting the regulatory roles of variants associated with OFCs, and the model can be extended to study other complex diseases or traits.

Unveiling Gene Interactions in Alzheimer's Disease by Integrating Genetic and Epigenetic Data with a Network-Based Approach.

Alzheimer's Disease (AD) is a complex disease and the leading cause of dementia in older people. We aimed to uncover aspects of AD's pathogenesis that may contribute to drug repurposing efforts by integrating DNA methylation and genetic data. Implementing the network-based tool, a dense module search of genome-wide association studies (dmGWAS), we integrated a large-scale GWAS dataset with DNA methylation data to identify gene network modules associated with AD. Our analysis yielded 286 significant gene network modules. Notably, the foremost module included the BIN1 gene, showing the largest GWAS signal, and the GNAS gene, the most significantly hypermethylated. We conducted Web-based Cell-type-Specific Enrichment Analysis (WebCSEA) on genes within the top 10% of dmGWAS modules, highlighting monocyte as the most significant cell type (p < 5 × 10-12). Functional enrichment analysis revealed Gene Ontology Biological Process terms relevant to AD pathology (adjusted p < 0.05). Additionally, drug target enrichment identified five FDA-approved targets (p-value = 0.03) for further research. In summary, dmGWAS integration of genetic and epigenetic signals unveiled new gene interactions related to AD, offering promising avenues for future studies.

A Single-Component Janus Zwitterionic Hydrogel Patch with a Bionic Microstructure for Postoperative Adhesion Prevention.

The development of anti-adhesion hydrogels for preventing postoperative adhesions is an ongoing challenge, particularly in achieving a balance between exceptional antifouling properties and effective in situ tissue retention. In this study, we propose a unique approach with the design of a single-component Janus zwitterionic hydrogel patch featuring a bionic microstructure. The Janus patches were prepared through free radical polymerization of sulfobetaine methacrylate with N, N'-methylenebis(2-propenamide) as the cross-linker. The incorporation of hexagonal facets separated by interconnecting grooves on one side imparts durable and reliable in situ retention capabilities to the Janus hydrogel patch when it is applied to traumatized tissues. The opposing flat surface exhibits outstanding resistance to bacteria, proteins, and cell adhesion, due to the superhydrophilicity and excellent antifouling characteristics of zwitterionic polymers. This dual functionality empowers the Janus hydrogel patch to mitigate adhesions between traumatized and surrounding tissues. The hexagonal and groove bionic microstructures facilitate rapid drainage, promoting swift contact with the tissue for increased adhesion strength, while independent hexagonal microfacets enhance the peeling energy. In an in vivo setting, Janus zwitterionic hydrogel patches with surface microstructures form mutually embedded structures with the cecum surface, minimizing the likelihood of slippage and detachment. Remarkably, in vivo experiments involving abdominal wall cecum injuries illustrate the Janus zwitterionic hydrogel patch's superior anti-adhesion effectiveness compared to commercial controls. Thus, the Janus hydrogel patch, distinguished by its bionic microstructure surface, presents substantial potential in the biomedical field for averting postoperative adhesions.

Immunomodulatory zymosan/ι-carrageenan/ agarose hydrogel for targeting M2 to M1 macrophages (antitumoral).

Several studies have been performed on the immunomodulatory effects of yeast ß-(1,3) glucan, but there is no proper evaluation of the thermal and immunomodulating properties of zymosan (ZM). Thermogravimetry analysis indicated a 54% weight loss of ZM at 270 °C. Circular dichroism showed absorption peaks in the region of 250 to 400 nm, suggesting a helical coil ß-sheet configuration. XRD showed a broad peak at 2θ of 20.38°, indicating the crystalline nature, and the size was found to be 23 nm. ZM is biocompatible and showed no toxicity against L929 and RAW 264.7 cell lines (cell viability > 90%). Immunomodulatory studies with PCR showed upregulation of M1 genes in human differentiated THP-1 macrophage cell lines, which were responsible for antitumor properties. The uptake of ZM particles inside the differentiated THP-1 macrophages and Raw 264.7 cells was confirmed (Video clip). ZM particle uptake via Dectin-1 was identified by competitive receptor blocking. Seaweed derived carrageenan/ZM/agarose hydrogel was successfully prepared (@5 : 5 wt%) and was seen to support the growth of L929 cells (1 × 105 cells per mL) and have a higher swelling (≈250-280%). This study indicates that ZM-based hydrogel could be a potential drug carrier (Rifampicin and Levofloxacin) for targeting tumour-associated macrophages (M2).

Montelukast as a repurposable additive drug for standard-efficacy multiple sclerosis treatment: Emulating clinical trials with retrospective administrative health claims data.

BACKGROUND: Effective and safe treatment options for multiple sclerosis (MS) are still needed. Montelukast, a leukotriene receptor antagonist (LTRA) currently indicated for asthma or allergic rhinitis, may provide an additional therapeutic approach. OBJECTIVE: The study aimed to evaluate the effects of montelukast on the relapses of people with MS (pwMS). METHODS: In this retrospective case-control study, two independent longitudinal claims datasets were used to emulate randomized clinical trials (RCTs). We identified pwMS aged 18-65 years, on MS disease-modifying therapies concomitantly, in de-identified claims from Optum's Clinformatics® Data Mart (CDM) and IQVIA PharMetrics® Plus for Academics. Cases included 483 pwMS on montelukast and with medication adherence in CDM and 208 in PharMetrics Plus for Academics. We randomly sampled controls from 35,330 pwMS without montelukast prescriptions in CDM and 10,128 in PharMetrics Plus for Academics. Relapses were measured over a 2-year period through inpatient hospitalization and corticosteroid claims. A doubly robust causal inference model estimated the effects of montelukast, adjusting for confounders and censored patients. RESULTS: pwMS treated with montelukast demonstrated a statistically significant 23.6% reduction in relapses compared to non-users in 67.3% of emulated RCTs. CONCLUSION: Real-world evidence suggested that montelukast reduces MS relapses, warranting future clinical trials and further research on LTRAs' potential mechanism in MS.