Search Results (1,277)

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system affecting over 2.8 million people around the world. Artificial intelligence (AI) is becoming increasingly utilised in many areas, including patient care for MS. AI is revolutionising the diagnosis and treatment of MS by enhancing the accuracy and efficiency of both processes. AI algorithms, particularly those based on machine learning, are being used to analyse medical imaging data, such as MRI scans, to detect early signs of MS, monitor disease progression and assess patient treatment response with greater precision. AI can help identify subtle changes in the brain and spinal cord that may be missed by human clinicians, leading to earlier diagnosis and more personalised treatment plans. Additionally, AI is being employed to predict disease outcomes, which could allow clinicians to tailor therapies for individual patients based on their unique disease characteristics. In drug development, AI is accelerating the identification of potential therapeutic targets and the optimisation of clinical trial designs, potentially leading to faster development of new treatments for MS. AI is also playing a critical role in MS fundamental research by promoting efficient analysis of vast amounts of single-cell data. Through these advancements, AI could improve the overall management of MS, offering more timely interventions and better patient outcomes. In this review, we discuss these topics and whether the influence of AI on diagnosis, treatment and research of MS can change the future of this field. Full article

Prostate cancer (PCa) is the most frequently diagnosed malignancy in the male genitourinary tract. However, the regulatory mechanism of competitive endogenous RNAs (ceRNAs) in PCa remains unclear. In this study, we first performed immune scores of mRNA data from 481 PCa samples using single-sample Gene Set Enrichment Analysis (ssGSEA). Based on the immune scores, we then evaluated the tumor immune microenvironment and analyzed 28 types of immune cells in PCa, we constructed a comprehensive network with four lncRNAs (MEG3, PCAT1, SNHG19, TRG-AS1), three miRNAs (hsa-miR-488-3p, hsa-miR-210-5p, hsa-miR-137), and twenty-seven mRNAs (including H2AFJ, THBS1, HPGD). Among the 28 immune cell types, seven immune cell types were found to be significantly associated with clinical characteristics. These network nodes have prognostic significance in multiple cancers and play critical roles in malignancy development, indicating the network’s predictive capability. We also observed a strong correlation (r = 0.6) between T-helper type 1 (Th1) cells and lncRNA network modules. The network connectivity highlights the association between immune therapy biomarkers for PCa, particularly those related to H2AFJ, THBS1, and HPGD. These findings provide valuable insights into the ceRNA regulatory network and its implications for immune-based therapies in PCa. Full article

Endothelial cells (ECs) are critical drivers of tumour progression, and their angiogenic process has been widely studied. However, the post-angiogenic transition of tip endothelial cells after sprouting remains insufficiently characterised. In this study, we utilised single-cell RNA sequencing analyses to identify a novel EC transition signature associated with endothelial permeability, migration, metabolism, and vascular maturation. Within the transition pathway, we discovered a critical EC subpopulation, termed tip-to-capillary ECs (TC-ECs), that was enriched in tumour tissues. Comparative analyses of TC-ECs with tip and capillary ECs revealed distinct differences in pathway activity, cellular communication, and transcription factor activity. The EC transition signature demonstrated substantial prognostic significance, validated across multiple cancer cohorts from TCGA data, particularly in bladder cancer. Subsequently, we constructed a robust prognostic model for bladder cancer by integrating the EC transition signature with multiple machine-learning techniques. Compared with 31 existing models across the TCGA-BLCA, GSE32894, GSE32548, and GSE70691 cohorts, our model exhibited superior predictive performance. Stratification analysis identified significant differences between different risk groups regarding pathway activity, cellular infiltration, and therapeutic sensitivity. In conclusion, our comprehensive investigation identified a novel EC transition signature and developed a prognostic model for patient stratification, offering new insights into endothelial heterogeneity, angiogenesis regulation, and precision medicine. Full article

Cellular senescence has emerged as a key mediator in organ-specific fibrosis. Here, we have established the role of endometrial stromal senescence in the progression of endometrial fibrosis, termed intrauterine adhesions (IUA). IUA have significant negative effects on women’s reproductive health and are associated with infertility. We have generated original gene signatures to identify endometrial stromal senescence in single-cell and bulk RNA-sequencing data. By applying generated gene signatures, we revealed an increased level of stromal senescence during the proliferative phase in the endometrium of patients with IUA. Further comparative analysis of cell–cell communications demonstrated that senescent stromal cells in the IUA endometrium create an immunosuppressive and profibrotic microenvironment through an elevated expression of LGALS9. Endometrial stromal senescence persists during the window of implantation and correlates with impaired embryo receptivity of the IUA endometrium. Therefore, stromal senescence can be regarded as a primary cause of an unresponsive endometrium with decreased receptivity and thickness in IUA patients. A LGALS9 immunotherapy protocol, specifically designed to neutralize LGALS9 immunosuppressive activity of senescent cells, may offer a promising opportunity to restore effective immune clearance of these cells within the IUA stroma. Consequently, an LGALS9-based strategy could emerge as a novel therapeutic avenue in the treatment of IUA. Full article

Background: Sepsis is a complex syndrome caused by a severe infection that occurs with a severe inflammatory response. Regulatory T cells (Tregs) have immunosuppressive effects and play a crucial role in modulating the immune response. There-fore, the number of Tregs is significantly increased in sepsis patients. Methods and Results: This paper aims to identify Tregs associated with the diagnosis of sepsis. For this purpose, transcriptional data from the GEO database for sepsis and its controls were downloaded and subjected to differential expression analysis. Immuno-infiltration analysis of the obtained DEGs revealed that Tregs were significantly different in sepsis and its controls. To further explore the cellular landscape and interactions in sepsis, single-cell RNA sequencing (scRNA-seq) data were analyzed. We identified key cell types and their interactions, including Tregs, using cell–cell communication analysis tools such as CellChat. This analysis provided in-sights into the dynamic changes in immune cell populations and their communication networks in sepsis. Thus, we utilized multiple machine learning algorithms to screen and extract Treg-related genes associated with sepsis diagnosis. We then performed both in-ternal and external validation tests. The final diagnostic model was constructed with high diagnostic accuracy (accuracy of 0.9615). Furthermore, we verified the diagnostic gene via a qPCR experiment. Conclusions: This paper elucidates the potential diagnostic targets associated with Tregs in sepsis progression and provides comprehensive understanding of the immune cell interactions in sepsis through scRNA-seq analysis. Full article

Background/Objectives: Acute obstructive pyelonephritis caused by ureteral calculi is a severe urological emergency that can rapidly progress to life-threatening complications, including urosepsis. Early risk stratification is critical for timely intervention and improved patient outcomes. Although Hu’s nomogram has been proposed as a predictive tool for urosepsis, its external validation remains limited. This study aims to validate Hu’s nomogram in an independent cohort and identify novel clinical and imaging predictors of urosepsis. Methods: This retrospective cohort study included 341 patients diagnosed with ureteral calculi who underwent surgical intervention at a single institution between January 2019 and October 2023. Clinical, laboratory, and imaging data were collected. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of urosepsis. The predictive accuracy of Hu’s nomogram was evaluated using receiver operating characteristic curve analysis. Results: Among 341 patients, 66 (19.4%) developed urosepsis. Multivariate analysis identified female gender, corticosteroid use, lower platelet count, elevated C-reactive protein levels, positive urine white blood cell count, lower computed tomography attenuation values of calculi, and higher computed tomography attenuation values of hydronephrosis as independent predictors of urosepsis. Hu’s nomogram demonstrated a strong predictive performance (area under the curve: 0.761; 95% CI: 0.701–0.821), reaffirming its clinical utility for risk stratification. Conclusions: This study provides an external validation of Hu’s nomogram and identifies novel risk factors for urosepsis prediction, including corticosteroid use and imaging-based parameters. Incorporating these findings into clinical practice may enhance early risk stratification, facilitate timely interventions, and ultimately improve patient outcomes. Full article

Aims: To investigate the effect of Glutathione metabolism in prostate cancer pathogenesis. Background: There is growing evidence that Glutathione metabolism plays an important role in prostate cancer, with genes encoding key enzymes in this pathway potentially serving as diagnostic or prognostic biomarkers. Objective: To explore whether there is a causal relationship between key enzymes in the Glutathione metabolism and prostate cancer, and to further investigate the molecular mechanisms and roles of the genes encoding their proteins in relation to prostate cancer. Method: Transcriptomic datasets from the Gene Expression Omnibus (GEO) database were analyzed to identify differentially expressed genes (DEGs) and enriched pathways in prostate cancer versus normal tissues. Two-sample bidirectional Mendelian randomization (MR) was employed to assess causal relationships between Glutathione metabolic enzymes (exposure) and prostate cancer risk (outcome). Immune infiltration analysis and LASSO regression were performed to construct a diagnostic model. Single-cell RNA sequencing (scRNA-seq) data were utilized to elucidate cell-type-specific expression patterns and functional associations of target genes. Result: The results of two-sample bidirectional MR showed that Glutathione S-transferase P (GSTP) in Glutathione metabolism could reduce the risk of prostate cancer. The Glutathione S-transferase Pi-1 (GSTP1) gene was lowly expressed in prostate cancer and was able to diagnose prostate cancer more accurately. Single-cell analysis showed that the high expression of GSTP1 in prostate cancer epithelial cells was closely associated with the upregulation of the P53 pathway and apoptosis. Conclusions: Our study reveals that GSTP in Glutathione metabolism reduces the risk of prostate cancer and further analyzes the genetic association and mechanism of action between GSTP1 and prostate cancer. Full article

Background/Objectives: Lipid metabolism plays a crucial role in uterine corpus endometrial carcinoma (UCEC); however, its specific mechanisms remain to be fully elucidated. This study aimed to construct a lipid-metabolism-related prognostic model and explore its association with the tumor immune microenvironment. Methods: A total of 552 UCEC and 35 normal tissue samples from The Cancer Genome Atlas (TCGA) database were analyzed to identify differentially expressed lipid-metabolism-related genes (DE-LMRGs). A prognostic risk model was established using univariate Cox analysis, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression, and its clinical utility was assessed through nomogram construction. Functional enrichment analysis was performed to explore the biological pathways involved. Tumor immune infiltration patterns were evaluated using single-sample Gene Set Enrichment Analysis (ssGSEA), Estimation of Stromal and Immune Cells in Malignant Tumors using Expression Data (ESTIMATE), and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms. Results: Multivariate analysis indicated that the prognostic model had robust predictive value, with AUCs of 0.701, 0.746, and 0.790 for 1-, 3-, and 5-year overall survival predictions. High-risk patients exhibited a suppressed immune microenvironment characterized by reduced immune cell infiltration, lower tumor mutation burden (TMB), and elevated TIDE scores, suggesting potential resistance to immunotherapy. Furthermore, LIPG was identified as a key hub gene through the intersection of nine machine learning algorithms, demonstrating strong associations with both cancer progression and immune infiltration. Functional validation using Cell Counting Kit-8 (CCK-8), wound healing, and transwell migration assays following small interfering RNA (siRNA) transfection demonstrated that LIPG promotes UCEC cell proliferation and migration in vitro. Conclusions: These findings highlight the critical role of lipid metabolism in UCEC progression and immune modulation, with LIPG emerging as a potential prognostic biomarker. The identified lipid-metabolism-related gene signature may provide new insights into tumor microenvironment interactions. Full article

Background: Cisplatin-based chemoradiotherapy is the standard treatment for locally advanced head and neck squamous-cell carcinoma (LA-HNSCC); however, the optimal dosing regimen remains debated. This systematic review and meta-analysis aimed to compare treatment compliance, therapeutic efficacy, and toxicity profiles between weekly (30–50 mg/m²) and triweekly (100 mg/m² every three weeks) cisplatin regimens in patients receiving concurrent radiotherapy for LA-HNSCC. Methods: A systematic literature search was conducted in PubMed, Google Scholar, and ClinicalTrials.gov to identify prospective clinical trials published before 16 January 2025, comparing weekly and triweekly cisplatin regimens. Studies were included if they reported treatment compliance, efficacy, and chemotherapy-related toxicities. Single-arm studies were excluded. Data extraction was performed independently by two reviewers, and the risk of bias was assessed using the Cochrane Risk of Bias (RoB) tool. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for the primary endpoints: overall survival (OS) and chemotherapy completion rates. Heterogeneity was assessed using the I² statistic. Results: Fifteen prospective clinical trials with 1572 patients (775 weekly and 797 triweekly) were included. Treatment compliance was similar between the regimens, with 74.76% (weekly) vs. 72.29% (triweekly) completing chemotherapy (p = 0.38). The mean cumulative cisplatin dose was significantly higher in the triweekly regimen (287.52 mg/m² vs. 241.74 mg/m², p = 0.04); however, the proportion of patients receiving a cumulative dose ≥200 mg/m² did not differ significantly (p = 0.23). The therapeutic efficacy was comparable, with complete response rates of 63.18% (weekly) and 67.13% (triweekly) (p = 0.32) and OS rates at 2 years of 51.24% and 49.47% (p = 0.45). No significant differences were observed in the toxicity rates (any grade or grade ≥ 3) or mortality. The I² statistic indicated insignificant heterogeneity across the studies. Interpretation: The results do not provide definitive evidence favoring one regimen over the other. Both regimens remain viable treatment options with comparable efficacy and adherence. Treatment selection should be individualized, considering toxicity risk, patient tolerability, and clinical factors. Full article

Objectives: This research aims to explore the therapeutic potential of Bi-Qi capsules in the treatment of gout by identifying crucial drug targets through a multidimensional data analysis strategy. Methods: Bi-Qi capsule drug targets and differentially expressed genes (DEGs) of gout were derived from public databases, such as Swiss Target Prediction, STITCH, and the GEO database. Subsequently, the overlapped targets were analyzed to elucidate the potential therapeutic mechanism and to identify candidate targets of Bi-Qi capsules against gout. Next, Mendelian randomization (MR) analysis was employed to screen and explore the causal relationship between candidate targets and gout. Finally, single-cell RNA sequencing (scRNA-seq), gene set enrichment analysis (GSEA), transcription factor and ceRNA regulatory networks, and molecular docking were performed to validate the role of the crucial targets of Bi-Qi capsules in the treatment of gout. Results: A total of 46 candidate targets were identified, in which KCNA5, PTGS2, and TNF exhibited significant causal relationships with gout (p < 0.05) and were regarded as the crucial targets. Through scRNA-seq and gene labeling, crucial targets were found to be expressed in eighteen cell clusters and eight cell types, which are closely associated with carbohydrate metabolism, nerve conduction, and the innate immunity process. Bi-Qi capsule active compounds such as tanshinone IIA, strychnine, tanshinaldehyde, cryptotanshinone, tumulosic acid, and glycyrrhetic acid exhibit a better binding ability to crucial targets. Conclusions: The results not only elucidate the anti-gout mechanism of Bi-Qi capsules but also provide an insight into multi-target natural medication for metabolic disease treatment, which contributes to guiding the clinical application of Bi-Qi capsules in the future. Full article

Necrotizing enterocolitis (NEC) is an acute intestine dysfunction intestinal disorder characterized by inflammation and cell death, including pyroptosis. Previous studies have implicated pyroptosis, particularly via NLRP3 inflammatory activation, and contribute to the development of NEC. However, the genetic and molecular mechanisms underlying pyroptosis in NEC pathogenesis and sequelae remain unclear. Our study aimed to identify the pyroptosis-related cell populations and genes and explore potential therapeutic targets. Single-cell RNA sequencing (scRNA-seq) data were analyzed to identify the cell populations related to NEC and pyroptosis. Weighted gene correlation network analysis (WGCNA) of bulk RNA-seq was performed to identify gene modules associate with pyroptosis. Cell–cell communication was employed to investigate intercellular signaling networks. Gene Set Enrichment Analysis (GSEA) was conducted to compare the pathways enriched in the high and low TREM1-expressing subgroups. Immunofluorescence staining was performed to detect the TREM1⁺CD163⁺ macrophages in the intestines. PCR and Western blot were performed to detect the expression of mRNA and proteins in the intestine tissues and cells. scRNA-seq analysis revealed increased macrophage abundance in NEC, with one macrophage cluster (cluster 4) exhibiting a markedly elevated pyroptosis score. WGCNA identified a gene module (MEbrown) that positively correlated with pyroptosis. Five genes (TREM1, TLN1, NOTCH2, MPZL1, and ADA) within this module were identified as potential diagnostic markers of pyroptosis. Furthermore, we identified a novel macrophage subpopulation, TREM1⁺CD163⁺, in NEC. Cell–cell communication analysis suggested that TREM1⁺CD163⁺ macrophages interact with other cells primarily through the NAMPT/ITGA5/ITGB1 and CCL3/CCR1 pathways. GSEA revealed a significant association between high TREM1 expression and pathways related to pyroptosis, cell proliferation, and inflammation. In vivo and in vitro experiments confirmed an increase in TREM1⁺CD163⁺ macrophages in NEC-affected intestines. TREM1 inhibition in THP-1 cells significantly reduced the expression of pro-inflammatory cytokines and pyroptosis-related genes and proteins. We identified the TREM1⁺CD163+ macrophage population that plays a crucial role in pyroptosis during NEC progression. Our findings elucidate the biological functions and molecular mechanisms of TREM1, demonstrating its upregulation in vivo and pro-pyroptosis effects in vitro. These insights advance our understanding of the role of pyroptosis in NEC pathogenesis and suggest TREM1 is a potential therapeutic target for NEC. Full article

The dynamic and meticulously regulated networks established the foundation for embryonic development, where the intercellular interactions and signal transduction assumed a pivotal role. In recent years, high-throughput technologies such as single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) have advanced dramatically, empowering the systematic dissection of cell-to-cell regulatory networks. The emergence of comprehensive databases and analytical frameworks has further provided unprecedented insights into embryonic development and cell–cell interactions (CCIs). This paper reviewed the exponential increased CCIs works related to developmental biology from 2008 to 2023, comprehensively collected and categorized 93 analytical tools and 39 databases, and demonstrated its practical utility through illustrative case studies. In parallel, the article critically scrutinized the persistent challenges within this field, such as the intricacies of spatial localization and transmembrane state validation at single-cell resolution, and underscored the interpretative limitations inherent in current analytical frameworks. The development of CCIs’ analysis tools with harmonizing multi-omics data and the construction of cross-species dynamically updated CCIs databases will be the main direction of future research. Future investigations into CCIs are poised to expeditiously drive the application and clinical translation within developmental biology, unlocking novel dimensions for exploration and progress. Full article

Sepsis is a severe systemic response to infection that may lead to the dysfunction of multiple organ systems and may even be life-threatening. Circadian rhythm-related genes (CRDRGs) regulate the circadian clock and affect many physiological processes, including immune responses. In patients with sepsis, circadian rhythms may be disrupted, thus leading to problems such as immune responses. RNA-seq datasets of sepsis and control groups were downloaded from the Gene Expression Omnibus (GEO) database, and two sepsis subtypes were identified based on differentially expressed CRDRGs. Two gene modules related to sepsis diagnosis and subtypes were obtained using the weighted co-expression network (WGCNA) algorithm. Subsequently, using four machine learning algorithms (random forest, support vector machine, a generalized linear model, and xgboost), genes related to sepsis diagnosis were identified from the intersection genes of the two modules, and a diagnostic model was constructed. Single-cell sequencing (scRNA-seq) data were obtained from the GEO database to explore the expression landscape of diagnostic-related genes in different cell types. Finally, an RT-qPCR analysis of diagnosis-related genes confirmed the differences in expression trends between the two groups. Multiple differentially expressed CRDRGs were observed in the sepsis and control groups, and two subtypes were identified based on their expression levels. There were apparent differences in the distribution of samples of the two subtypes in two-dimensional space and the pathways involved. Using multiple machine learning algorithms, the intersection genes in the two most relevant modules of the WGCNA were identified, and a robust diagnostic model was constructed with five genes (ARHGEF18, CHD3, PHC1, SFI1, and SPOCK2). The AUC of this model reached 0.987 on the validation set, showing an excellent prediction performance. In this study, two sepsis subtypes were identified, and a sepsis diagnostic model was constructed via consensus clustering and machine learning algorithms. Five genes were identified as diagnostic markers for sepsis and can thus assist in clinical diagnosis and guide personalized treatment. Full article

Background/Objectives: Over the past three decades, significant progress has been made in understanding the biology of neuroblastoma. The integration of prognostic factors has facilitated risk stratification and the development of targeted treatment approaches. This study aims to analyze the outcomes of pediatric patients with neuroblastoma treated at a single oncology center over a 30-year period. Methods: This retrospective study analyzed data from patients aged 0–18 years with neuroblastoma, treated at the Department of Pediatric Hematology and Oncology in Bydgoszcz, Poland, between 1993 and 2023. The study endpoints included the 5-year probability of overall survival (pOS), event-free survival (pEFS), and relapse-free survival (pRFS), analyzed separately for low/intermediate- and high-risk groups. Results: Seventy-five patients met the inclusion criteria. Thirty-two children were categorized as high-risk patients and forty-three as low/intermediate risk. During the study period, outcomes in the low/intermediate-risk group improved significantly (the 5-year pOS 85.7% vs. 100.0%, p = 0.019; the 5-year pRFS 85.7% vs. 100.0%, p = 0.662; the 5-year pEFS 83.3% vs. 100.0%, p = 0.038). In the high-risk group, outcomes improved but did not reach statistical significance (the 5-year pOS 0.0% vs. 41.1%, p = 0.342; the 5-year pRFS 0.0% vs. 32.5%, p = 0.180; and the 5-year pEFS 0.0% vs. 21.5%, p = 0.537). Sixteen patients experienced relapse, of whom only three survived; stem cell transplantation at relapse significantly improved survival (OS 0.0% vs. 50.0%, p = 0.001). In the multivariable analysis, stage at diagnosis was a prognostic factor for pOS (HR 6.0; 95%CI 0.7–49.6, p = 0.096), while pelvic localization was a risk factor for pRFS (HR 3.0; 95%CI 0.8–10.5; p = 0.084). Conclusions: This analysis highlights significant advancements in the diagnosis and treatment of neuroblastoma. Nevertheless, outcomes for high-risk patients and those who experience relapse remain poor, underscoring the need for further therapeutic improvements. Full article

The relevance of the current study is to increase the resistance of fungal biofilms to traditional disinfection methods. The aim of the study was to determine how effectively Er:YAG laser light inhibits single-species Candida biofilms. The study involved a systematic review of 57 scientific publications (2015–2024) selected according to specific criteria, followed by an assessment of quantitative and qualitative indicators of colony-forming unit reduction. The results show that under optimal parameters (power 1.5–3.9 W and duration 60–90 s), the Er:YAG laser can reduce the number of viable Candida albicans cells by an average of 70–90%, and when combined with sodium hypochlorite or chlorhexidine solutions, this figure can exceed 90%. Separate in vitro tests show that Candida glabrata and Candida tropicalis require higher power or longer exposure to achieve a similar effect, while the use of the Er:YAG laser on titanium and dental surfaces minimizes damage to the substrate and effectively removes the biofilm matrix. In addition, laser treatment accelerates tissue regeneration and helps reduce the number of cases of reinfection, which is confirmed by the positive dynamics in clinical practice. Data analysis using confocal microscopy and microbiological seeding indicates a significant disruption of the biofilm structure and increased permeability to antimycotics after laser exposure. Er:YAG laser disinfection method is promising in counteracting fungal biofilms, especially for surfaces with a high risk of microbial colonization. The practical value lies in the possibility of developing standard protocols for the clinical use of the laser, which will increase the effectiveness of treatment and prevention of Candidal lesions. Full article