Search Results (2,130)

This study investigated the role of molecular mimicry in the context of autoimmunity associated with viral infection, using SARS-CoV-2 as a model system. A bioinformatic analysis was performed to identify sequence homologies between the SARS-CoV-2 Spike (S) protein and the human proteome, with a specific focus on immunogenic regions to assess potential cross-reactivity. The analysis revealed homologous regions between the viral S protein and several human proteins, including DAAM2, CHL1, HAVR2/TIM3, FSTL1, FHOD3, MYO18A, EMILIN3, LAMP1, and αENaC, which are predicted to be recognizable by B-cell receptors. Such recognition could potentially lead to the production of autoreactive antibodies, which can contribute to the development of autoimmune diseases. Furthermore, the study examined potential autoreactive CD4+ T-cell responses to human protein autoepitopes that could be presented by HLA class II molecules. Several HLA class II genetic variants were computationally associated with a higher likelihood of cross-reactive immune reactions following COVID-19, including HLA-DPA1*01:03/DPB1*02:01, HLA-DPA1*02:01/DPB1*01:01, HLA-DPA1*02:01/DPB1*05:01, HLA-DPA1*02:01/DPB1*14:01, HLA-DQA1*01:02/DQB1*06:02, HLA-DRB1*04:01, HLA-DRB1*04:05, HLA-DRB1*07:01, and HLA-DRB1*15:01. Additionally, seven T helper cell autoepitopes (YSEILDKYFKNFDNG, ERTRFQTLLNELDRS, AERTRFQTLLNELDR, RERKVEAEVQAIQEQ, NAINIGLTVLPPPRT, PQSAVYSTGSNGILL, TIRIGIYIGAGICAG) were identified that could be implicated in autoimmune T-cell responses through presentation by class II HLA molecules. These findings highlight the utility of viral B- and T-cell epitope prediction for investigating molecular mimicry as a possible mechanism in virus-associated autoimmunity. Full article

Triple-negative breast cancer (TNBC) is one of the most aggressive subtypes of breast cancer (BC), comprising approximately 20% of newly diagnosed BC cases. The poor prognosis, high recurrence rates, and inefficacy of hormone-based therapies make TNBC one of the greatest challenges in contemporary oncology. The unique immunological features of TNBC, including relatively high tumor mutational burden, abundance of tumor-infiltrating lymphocytes, and elevated PD-L1 expression, offer a wide range of opportunities for immunotherapeutic approaches, of which the most progressive and promising are neoantigen-driven ones. This review examines the current landscape of neoantigen-based therapeutic approaches in TNBC treatment, spanning from discovery methodologies to clinical applications. We provide a critical analysis of the tumor microenvironment (TME) in TNBC, highlighting the balance between its immunoactivating (CD8+ T-cells, dendritic cells) and immunosuppressive (regulatory T-cells, M2 macrophages) components as the key determinant of therapeutic success, as well as reviewing the emerging approaches to TME reprogramming and recruiting in favor of better outcomes. We also present state-of the-art methods in neoantigen identification and prioritization, covering the landscape of technological platforms and prediction algorithms, addressing the existing accuracy limitations along with emerging computational solutions, and comprehensively discussing the TNBC neoantigen spectrum. Our analysis shows the strong domination of patient-specific (“private”) neoantigens over shared variants in the TNBC, with TP53 as the only gene with recurrent variants. Finally, we extensively cover neoantigen-recruiting therapeutic modalities including adoptive cell therapies, personalized vaccine platforms (peptide-based, mRNA/DNA vaccines, dendritic cell vaccines), and oncolytic viruses-based approaches. Our study of current clinical trials demonstrates the substantial gap between early proof-of-concept experiments and further applicability of neoantigen-driven therapies. The major challenges hampering the success of such methods include neoantigen prediction inaccuracy rates, high manufacturing costs, and time consumption. Promising ways to overcome these difficulties include the development of combinational strategies, TME modeling and modifying, and improvement of the therapy delivery properties, along with the optimization of production workflows and cost-effectiveness of vaccine development. Full article

Background: Both lipid metabolism disorders and inflammation are critical contributors to the progression of metabolic-associated fatty liver disease (MAFLD), yet integrated analyses identifying key genes linking them remain scarce. Methods: Differentially expressed genes in MAFLD were extracted from the GSE135251 dataset and intersected with lipid metabolism- and inflammation-related genes from Molecular Signatures Database (MSigDB). Machine learning on GSE135251, followed by validation on GSE89632, identified key genes. Functional enrichment, immune microenvironment profiling, and nomogram analysis were subsequently conducted. Cellular heterogeneity was assessed using the single-cell sequencing (scRNA-seq) dataset GSE186328, and gene expression in MAFLD mice was validated via real-time Polymerase Chain Reaction (PCR). Activators targeting these genes were predicted using Drug Signatures Database (DsigDB). Results: Four genes—FADS1, FADS2, GLB1, and PNPLA3—were identified as key regulators involved in both lipid metabolism disorders and inflammation in MAFLD. These genes were co-enriched in ribosome-related pathways. GLB1 correlated strongly with CD56dim natural killer cells in immune infiltration analysis. A diagnostic nomogram integrating these genes demonstrated exceptional discriminatory power, with Area Under the Curve (AUC) values of 0.98981 for GSE135251 and 0.9204 for GSE89632. ScRNA-seq revealed elevated FADS1, FADS2, and GLB1 expression in MAFLD-associated NK/T cells compared to controls. Real-time PCR confirmed significant upregulation of all four genes in MAFLD mice. Drug prediction identified estradiol as a potential activator targeting these genes. Conclusions: This study identified FADS1, FADS2, GLB1, and PNPLA3 as key genes involved in the progression of MAFLD, linking metabolic dysfunction and inflammation. Full article

Human rhinovirus (RV) is the most frequent cause of the common cold, as well as severe exacerbations of chronic obstructive pulmonary disease (COPD) and asthma. Currently, there are no effective and accurate diagnostic tools or antiviral therapies. MicroRNAs (miRNAs) are small, non-coding sections of RNA involved in the regulation of gene expression and have been shown to be associated with different pathologies. However, the precise role of miRNAs in RV infection is not yet well established. Also, no unified computational framework exists to specifically link miRNA expression with functional gene targets during RV infection. This study aimed to first analyse the impact of RV16 on miRNA expression across the viral life cycle to identify a small panel with altered expression. We then developed a novel bioinformatics pipeline that integrated time-resolved miRNA profiling with multi-database gene-phenotype mapping to identify diagnostic biomarkers and their regulatory networks. Our in-house Python-based tool, combining mirDIP, miRDB and VarElect APIs, predicted seven genes (EZH2, RARG, PTPN13, OLFML3, STAG2, SMARCA2 and CD40LG) implicated in antiviral responses and specifically targeted by RV16 and regulated by our miRNAs. This method therefore offers a scalable approach to interrogate miRNA-gene interactions for viral infections, with potential applications in rapid diagnostics and therapeutic target discovery. Full article

Immune-effector-cell-associated hematotoxicity has emerged as the most common CAR-T-cell-related complication in the real-world setting. Therefore, transfusion of blood components remains unavoidable in many patients treated with CAR-T cells to alleviate symptomatic anemia and prevent major bleeding events. This study investigates predictive factors associated with the transfusion requirement in patients receiving anti-CD19 CAR-T-cell therapy for B-cell malignancies in a real-world setting and the potential correlation between transfusion needs, ICAHT, and long-term survival outcomes. Among 90 investigated patients, 51 (56.7%) received at least one transfusion in the three months post-infusion (33.4% received only RBC concentrates, and 23.4% received both RBC and platelet transfusions). The highest transfusion needs occurred in the first month post-infusion, with 50 transfused patients (55.5%). Early transfusion-requiring cytopenia was associated with pre-infusion altered bone marrow function, patients-related factors, including female sex, and acute inflammatory toxicities. The incidence of late cytopenia was mainly predicted by the need for pre-infusion transfusion support. Patients receiving platelet transfusions were characterized by an inferior progression-free (p = 0.013) and overall survival (p = 0.005). CAR-T-cell-treated patients can experience a high transfusion burden, impairing their quality of life, potentially affecting survival outcomes, and resulting in overutilization of clinical resources Full article

Public building-attached green spaces are increasingly important urban carbon sinks, yet their carbon sequestration potential remains poorly understood and underutilized. This study quantified vegetation carbon storage across three attached green space typologies (green square, roof garden, and sunken courtyard) at a representative public building in Wuhan, China, using field surveys and species-specific allometric equations. Total carbon storage reached 19,873.43 kg C, dominated by the green square (84.98%), followed by a roof garden (12.29%) and sunken courtyard (2.72%). Regression analysis revealed strong correlations between carbon storage and morphological traits, with diameter at breast height (DBH) showing the highest predictive power for trees (r = 0.976 for evergreen, 0.821 for deciduous), while crown diameter (CD) best predicted shrub carbon storage (r = 0.833). Plant configuration optimization strategies were developed through correlation analysis and ecological principles, including replacing low carbon sequestering species with high carbon native species, enhancing vertical stratification, and implementing multi-layered planting. These strategies increased total carbon storage by 131.5% to 45,964.00 kg C, with carbon density rising from 2.00 kg C∙m⁻² to 4.63 kg C∙m⁻². The findings provide a quantitative framework and practical strategies for integrating carbon management into the design of building-attached green spaces, supporting climate-responsive urban planning and advancing sustainable development goals. Full article

Background/Objectives: Crohn’s disease (CD) is one of the most frequent causes of short bowel syndrome (SBS), a severe clinical condition with huge morbidity and social costs. SBS occurs when, following intestinal resections, the remaining small bowel in continuity is less than 200 cm in length. Intestinal failure (IF) can complicate SBS when intravenous nutritional or electrolyte supplementation is required to maintain dietary needs. The primary aim of this study was to identify clinical predictive factors of SBS in a cohort of outpatients with CD. Methods: We conducted a prospective, single-center, cohort study enrolling consecutive CD outpatients at a tertiary-level inflammatory bowel disease center. Detailed demographic and clinical features were collected. Significant factors associated with the onset of SBS in the univariate analysis were input into a multivariate logistic regression model to identify independent predictors of SBS. Results: In total, 232 CD patients (52.6% male, median age 49 years [IQR 37–60]) were included: 24.6% of them were smokers; extraintestinal manifestations (EIMs) were present in 21.6% of patients; and 67.7% of patients had at least one intestinal resection (27% of them with more than one surgical intervention). At enrollment, 96.1% of patients were on advanced therapies, and considering the course of the disease, 24.6% of patients were exposed to ≥3 different advanced therapies. A total of 18 patients had SBS and 9 had IF. In univariate analysis, the following variables were statistically associated with the risk of developing SBS: disease duration (p < 0.001), upper gastrointestinal disease localization (L4) (p < 0.001), penetrating behavior (p = 0.023), perianal disease (p = 0.036), length of first intestinal resection (p < 0.001), shorter time elapsing from CD diagnosis to start the first advanced therapy (p < 0.001), and treatment with advanced therapy after first intestinal resection (p < 0.001). In multivariate analysis, disease duration (OR 1.083, 95% C.I. 1.025–1.145, p = 0.005) and L4 (OR 20.079, 95% C.I. 2.473–163.06, p = 0.005) were independently associated with the development of SBS. Conversely, the number of different advanced therapies before the onset of SBS was independently associated with a reduced risk of developing SBS (OR 0.247, 95% C.I. 0.107–0.58, p = 0.001). Conclusions: Our data identifies several clinical features that could possibly predict the development of SBS in CD. Further studies with a larger sample size are needed to confirm our findings. Full article

CD19-targeted therapies, including monoclonal antibodies, antibody–drug conjugates, and chimeric antigen receptor (CAR) T-cell products, have significantly improved outcomes in relapsed/refractory diffuse large B-cell lymphoma (R/R DLBCL). Despite their clinical efficacy, resistance and antigen modulation pose substantial challenges, especially in patients requiring sequential therapy. This review provides a comprehensive overview of CD19 biology and its relevance as a therapeutic target. We examine mechanisms of resistance such as antigen loss, epitope masking, and T-cell exhaustion, as well as the implications of tumor microenvironmental immunosuppression. Future efforts should prioritize the integration of real-time diagnostics, such as flow cytometry, immunohistochemistry, and transcriptomic profiling, and AI-assisted predictive models to optimize therapeutic sequencing and expand access to personalized immunotherapy. Full article

Ivermectin (IVM), a broad-spectrum anthelmintic agent, has anti-inflammatory properties, and affects cellular and humoral immune responses. We recently showed that multiple pro-inflammatory cytokines (e.g., FGF2, CCL5, CD40L) bind to the allosteric site (site 2) of integrins and activate them. 25-Hydroxycholesterol, a pro-inflammatory lipid mediator, is known to bind to site 2 and induce integrin activation and inflammatory signals (e.g., IL-6 and TNF secretion), suggesting that site 2 is critically involved in inflammation. We showed that two anti-inflammatory cytokines (FGF1 and NRG1) bind to site 2 and inhibit integrin activation by inflammatory cytokines. We hypothesized that ivermectin binds to site 2 and inhibits inflammatory signaling by pro-inflammatory cytokines. A docking simulation predicts that ivermectin binds to site 2. Ivermectin inhibits the integrin activation induced by inflammatory cytokines, suggesting that ivermectin is a site 2 antagonist. We showed that TNF, a major pro-inflammatory cytokine, binds to integrin site 2 and induces allosteric integrin activation like other pro-inflammatory cytokines, suggesting that site 2 binding and integrin activation is a potential mechanism of the pro-inflammatory action of these cytokines. Ivermectin suppressed the activation of soluble β3 integrins by TNF and other pro-inflammatory cytokines in a dose-dependent manner in cell-free conditions. Binding to site 2 and the inhibition of binding of inflammatory cytokines may be a potential mechanism of anti-inflammatory action of ivermectin. Full article

Background/Objectives: Nutritional status affects postoperative outcomes, but the effect of standardized preoperative nutritional preparation on morbidity in malnourished patients undergoing pancreatoduodenectomy (PD) remains unclear. This study evaluated preoperative nutritional parameters following the standardization of nutritional screening and intervention in patients undergoing PD. The influence of nutritional parameters on postoperative morbidity was also assessed. Methods: This prospective cohort study was conducted from 2019 to 2021 at the Department of Surgery, University Hospital, Olomouc. A total of 133 patients were categorized nutritionally as “high risk” (weight loss or reduced appetite with restricted intake) or “low risk” (no weight or appetite loss). High-risk patients received enteral supplementation of 600 kcal/day. A multivariate logistic regression model was used to evaluate the association between major postoperative complications and risk factors, including sex, age, ASA score, BMI, weight and appetite loss, malignancy, duct diameter, pancreatic texture, serum albumin, prealbumin, MUST, and NRS2002 scores. Results: Eighty patients (60.2%) were “high risk,” and 53 (39.8%) were “low risk.” Major morbidity and 90-day mortality occurred in 24 (18.0%) and 4 (3.0%) patients, respectively. No significant differences were observed between high- and low-risk groups in CD morbidity grade, 90-day mortality, POPF, PPH, DGE, or hospital stay. Major morbidity was associated with prealbumin < 0.2 g/L, duct diameter ≤ 3 mm, soft texture, and male sex, with respective odds ratios of 3.307, 3.288, 4.814, and 2.374. Conclusions: High-risk patients receiving preoperative nutrition had comparable rates of major complications and POPF as low-risk patients. Low serum prealbumin predicts major postoperative complications after PD. Full article

The gut microbiome has emerged as a critical determinant of immune-checkpoint inhibitor (ICI) efficacy. A narrative review of 95 clinical studies (2015–2025) shows that patients with greater gut microbial diversity and relative enrichment of commensals such as Akkermansia, Ruminococcus, and other short-chain fatty acid producers experience longer progression-free and overall survival, particularly in melanoma and non-small-cell lung cancer. Broad-spectrum antibiotics given within 30 days of ICI initiation and over-the-counter mixed probiotics consistently correlate with poorer outcomes. Early phase I/II trials of responder-derived fecal microbiota transplantation in ICI-refractory melanoma achieved objective response rates of 20–40%, while pilot high-fiber or plant-forward dietary interventions improved immunologic surrogates such as CD8⁺ tumor infiltration. Machine-learning classifiers that integrate 16S or metagenomic profiles predict ICI response with an area under the ROC curve of 0.83–0.92. Methodological heterogeneity across sampling, sequencing, and clinical endpoints remains a barrier, underscoring the need for standardization and larger, well-powered trials. Full article

Background/Objectives: Hematopoietic stem cell (HSCs) mobilization from the bone marrow to the peripheral blood (PB) is a critical step in stem cell transplantation. Although some experimental studies have suggested that cholesterol levels may affect this process, the clinical relevance of lipid profiles in healthy donors remains unclear. This study aimed to investigate whether serum cholesterol parameters are associated with peripheral blood CD34+ HSC mobilization in healthy stem cell donors. Methods: A total of 251 healthy donors who underwent granulocyte colony-stimulating factor (G-CSF)-based mobilization were retrospectively analyzed. Peripheral blood CD34+ cell counts and yields (×106/kg) were recorded. Laboratory parameters, including total cholesterol, HDL-C, LDL-C, and triglyceride levels were evaluated. Correlations between mobilization outcomes and donor characteristics or laboratory findings were also assessed. Results: No significant association was found between serum lipid parameters (total cholesterol, LDL-C, HDL-C, triglycerides) and CD34+ cell mobilization or yield. However, white blood cell count, hemoglobin level, platelet count, absolute neutrophil count, and lymphocyte count showed significant positive associations with mobilization efficacy. In contrast, body mass index (BMI) was inversely correlated with CD34+ cell yield. Conclusions: Serum cholesterol levels do not appear to influence stem cell mobilization outcomes in healthy donors. Classical hematologic parameters remain reliable predictors of CD34+ cell yield. These findings suggest that cholesterol is not a suitable biomarker for predicting mobilization efficiency in this population group. Full article

Local scour around bridge piers is one of the primary causes of structural failure in bridges. Therefore, this study focuses on addressing the estimation of maximum scour depth (d_sm), which is essential for safe and resilient bridge design. Many studies in the last eight decades have included metadata collection and developed around 80 empirical formulas using various scour-affecting parameters of different ranges. To date, a total of 33 formulas have been comparatively analyzed and ranked based on their predictive accuracy. In this study, novel formulas using semi-empirical methods and gene expression programming (GEP) have been developed alongside an artificial neural network (ANN) model to accurately estimate d_sm using 768 observed data points collected from published work, along with eight newly conducted experimental data points in the laboratory. These new formulas/models are systematically compared with 74 empirical literature formulas for their predictive capability. The influential parameters for predicting d_sm are flow intensity, flow shallowness, sediment gradation, sediment coarseness, time, constriction ratio, and Froude number. Performances of the formulas are compared using different statistical metrics such as the coefficient of determination, Nash–Sutcliffe efficiency, mean bias error, and root-mean-squared error. The Gauss–Newton method is employed to solve the nonlinear least-squares problem to develop the semi-empirical formula that outperforms the literature formulas, except the formula from GEP, in terms of statistical performance metrics. However, the feed-forward ANN model outperformed the semi-empirical model during testing and validation phases, respectively, with higher CD (0.790 vs. 0.756), NSE (0.783 vs. 0.750), lower RMSE (0.289 vs. 0.301), and greater prediction accuracy (64.655% vs. 61.935%), providing approximately 15–18% greater accuracy with minimal errors and narrower uncertainty bands. Using user-friendly tools and a strong semi-empirical model, which requires no coding skills, can assist designers and engineers in making accurate predictions in practical bridge design and safety planning. Full article

Proteasome subunit beta type-9 (PSMB9), a member of the proteasome beta subunit family, encodes the pivotal β1i component of the immunoproteasome. PSMB9 plays a crucial role in antigen processing and presentation; however, its comprehensive role in orchestrating a tumor-immune landscape and regulating the anti-tumor immune responses remains unexplored. Here we investigated the context-dependent functions of PSMB9 by integrating multi-omics data from The Cancer Genome Atlas, Genotype-Tissue Expression database, Human Protein Atlas, Tumor Immunotherapy Gene Expression Resource, and multiple other databases. Moreover, we explored the predictive value of PSMB9 in multiple immunotherapy cohorts and investigated its functional relevance in CAR-T therapy using genome-scale CRISPR/Cas9 screening, gene knockout cell line in vitro, and clinical cohort validation. We found widespread dysregulation in PSMB9 across cancers, predominantly upregulated in most malignancies and associated with advanced pathological stages in specific contexts. PSMB9 was also broadly and negatively correlated with tumor stemness indices. Crucially, PSMB9 expression was robustly linked to anti-tumor immunity by being significantly correlated with immune-pathway activation (e.g., IFN response, cytokine signaling), immune regulatory and immune checkpoint gene expression, and enhanced infiltration of T cells across nearly all tumor types. Consequently, elevated PSMB9 predicted superior response to immune checkpoint inhibitors in multiple cohorts, showing comparable predictive power to established predictive signatures. Furthermore, CRISPR/Cas9 screening identified PSMB9 loss as a novel mechanism of resistance to CD19 CAR T cell therapy, with PSMB9-deficient tumor cells exhibiting a survival advantage under CAR-T pressure, supported by trends in clinical CAR-T outcomes. Our study uncovers PSMB9 as a previously unrecognized critical regulator of the tumor immune landscape in a pan-cancer scope, whose expression orchestrates key immune processes within the tumor microenvironment and serves as a potent biomarker for patient prognosis. Critically, we first established PSMB9 as a novel prognostic indicator for both checkpoint blockade and CAR-T cell therapies, highlighting its dual role as a crucial immune modulator and a promising biomarker for guiding T cell-based immunotherapy strategies across diverse human cancers. Full article

Soil element background concentration is foundational data for environmental quality assessment, contamination diagnosis, and sustainable land management. However, existing investigation-based methods are time-consuming and inefficient. The machine learning (ML) method has demonstrated excellent performance in predicting soil heavy metal concentration. In this study, based on the nine environmental variables of soil formation from 210 soil monitoring points, including elevation, pH, organic matter, soil type, parent material, plant cover, land use type, topography, and soil texture, decision tree (DT), random forest (RF), extreme gradient boosting (XGB), and support vector machine (SVM) models were used to predict the eleven soil element background concentrations. Among them, SVM and RF models could be used for an effective prediction of the background concentration of all soil heavy metals. Compared with the XGBoost and DT, the SVM for all heavy metals except for cadmium (Cd) and manganese (Mn) performs best. Although the key factors affecting background concentrations vary among different soil elements, organic matter, soil type, and altitude, they play a crucial role in the accurate prediction of soil element background concentration. This study provides simple and efficient ML models for predicting soil element background concentration at the large regional scale. The results of this study can be utilized to distinguish natural geochemical processes from human-induced pollution. Full article