Search Results (732)

Gastrointestinal (GI) malignancies are diverse and particularly challenging in terms of current immunotherapy but hold great opportunity for impact given that they constitute the highest cancer incidence and mortality rates worldwide. Traditional treatment options for solid GI malignancies include surgical intervention, chemotherapy, radiation, or a combination of these treatments. Emerging modalities within immunotherapy are anticipated to extend the results with conventional therapy by stimulating the patient’s own intrinsic potential for tumor-specific immunologic rejection. Combination regimens of chemotherapy and tumor-infiltrating lymphocyte (TIL) therapy in advanced colorectal cancer and pancreatic cancer, autologous monocyte therapy in advanced gastric cancer, and CAR-T therapy trained against GI-selective tumor antigens such as carcinoembryonic antigen are currently being studied. Clinical trials are underway to study the combination of various chemotherapeutic agents along with immunotherapy in the management of cholangiocarcinoma, hepatocellular carcinoma, and esophageal cancer. Alternative therapies are needed based on the tumor immune microenvironment, which can lead to a personalized approach to treatment. In this review, we discuss the current status of various modalities of immunotherapy in common GI malignancies, along with their mechanisms of immune activation and cancer suppression. We will also discuss the use of immunotherapy in less common solid GI malignancies and touch on recent advancements and clinical trials. Full article

Background: Pharmacy technicians are increasingly involved in immunization services, enhancing vaccine accessibility and reducing pharmacies’ workload. This scoping review aims to (1) provide a comprehensive overview of pharmacy technicians’ involvement in immunization services across various healthcare settings and countries, and (2) conduct a comparative analysis of training curricula for pharmacy technicians on immunization. Methods: A scoping review was conducted following the Arksey and O’Malley framework. A comprehensive search of the PubMed and Scopus databases was performed using keywords and MeSH terms such as “pharmacy technician(s)”, “immunization”, “vaccination”, “role”, and “involvement”. Studies included assessed pharmacy technicians’ roles in vaccine administration, training, and public health outcomes. Descriptive and thematic analyses were used to synthesize the findings. In addition, a supplementary analysis of immunization training curricula was conducted, reviewing programs from different countries to identify similarities, differences, and gaps in course structure, content, and delivery formats. Lastly, a comprehensive toolkit was developed, offering guidelines intended to facilitate the implementation of immunization training programs. Results: A total of 35 articles met the inclusion criteria, primarily from the United States of America (n = 30), Canada (n = 2), Ethiopia (n = 1), Denmark (n = 1) and United Kingdom (n = 1). The findings indicate that pharmacy technicians contribute significantly to vaccine administration, patient education, and workflow optimization, particularly in community pharmacies. The COVID-19 pandemic accelerated their involvement in immunization programs. Key challenges include regulatory barriers, a lack of standardized training, and resistance from other healthcare professionals. Facilitators include legislative support (e.g., the PREP Act), structured training programs, and collaborative pharmacist–technician models. Conclusions: Pharmacy technicians can play a vital role in expanding immunization services, improving vaccine uptake, and reducing pharmacist workload. Addressing regulatory inconsistencies, enhancing training, and fostering interprofessional collaboration are crucial for their effective integration of immunization programs. Since immunization by pharmacy technicians is not yet allowed in many EU countries, this review will provide a foundational basis to address their potential to support the healthcare workforce and improve access to immunization services. Full article

Vision-based end-to-end navigation systems have shown impressive capabilities, especially when combined with Imitation Learning (IL) and advanced Deep Learning architectures, such as Transformers. One such example is CIL++, a Transformer-based architecture that learns to map navigation states to vehicle controls based on expert demonstrations only. Nevertheless, reliance on experts’ datasets limits generalization and can lead to failures in unknown circumstances. Deep Reinforcement Learning (DRL) can address this issue by fine-tuning the pretrained policy, using a reward function that aims to improve its weaknesses through interaction with the environment. However, fine-tuning with DRL can lead to the Catastrophic Forgetting (CF) problem, where a policy forgets the expert behaviors learned from the demonstrations as it learns to optimize the new reward function. In this paper, we present CILRLv3, a DRL-based training method that is immune to CF, enabling pretrained navigation agents to improve their driving skills across new scenarios. Full article

Background: Glioblastoma (GBM) is one of the most challenging malignancies in all of neoplasms. These malignancies are associated with unfavorable clinical outcomes and significantly compromised patient wellbeing. The immunological landscape within the tumor microenvironment (TME) plays a critical role in determining GBM prognosis. By mining data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and correlating them with immune responses in the TME, genes associated with the immune microenvironment with potential prognostic value were obtained. Method: We selected GSE16011 as the training set. Gene expression profiles were substrates scored by both ESTIMATE and xCell, and immune cell subpopulations in GBM were analyzed by CIBERSORT. Gene expression profiles associated with low immune scores were performed by lasso regression, Cox analysis and random forest (RF) to identify a prognostic model for the multiple genes associated with immune infiltration in GBM. Then we constructed a nomogram to optimize the prognostic model using GSE7696 and TCGA-GBM as validation sets and evaluated these data for gene mutation and gene enrichment analysis. Result: The prognostic correlation between the six genes (MEOX2, PHYHIP, RBBP8, ST18, TCF12, and THRB) and GBM was finally found by lasso regression, Cox regression, and RF, and the online database obtained that all six genes were differentially expressed in GBM. Therefore, a prognostic correlation model was constructed based on the six genes. Kaplan–Meier (KM) survival analysis showed that this prognostic model had excellent prognostic ability. Conclusions: Prognostic models based on tumor microenvironment and immune score stratification and the construction of related genes have potential applications for prognostic analysis of GBM patients. Full article

High rates of childhood vaccination defaulting remain a significant barrier to achieving full vaccination coverage in sub-Saharan Africa, contributing to preventable morbidity and mortality. This study evaluated the utility of machine learning algorithms for predicting childhood vaccination defaulters in Ghana, addressing the limitations of traditional statistical methods when handling complex, high-dimensional health data. Using a merged dataset from two malaria vaccine pilot surveys, we engineered novel temporal features, including vaccination timing windows and birth seasonality. Six algorithms, namely logistic regression, support vector machine, random forest, gradient boosting machine, extreme gradient boosting, and artificial neural networks, were compared. Models were trained and validated on both original and synthetically balanced and augmented data. The results showed higher performance across the ensemble tree classifiers. The random forest and extreme gradient boosting models reported the highest F1 scores (0.92) and AUCs (0.95) on augmented unseen data. The key predictors identified include timely receipt of birth and week six vaccines, the child’s age, household wealth index, and maternal education. The findings demonstrate that robust machine learning frameworks, combined with temporal and contextual feature engineering, can improve defaulter risk prediction accuracy. Integrating such models into routine immunization programs could enable data-driven targeting of high-risk groups, supporting policymakers in strategies to close vaccination coverage gaps. Full article

Triple-negative breast cancer (TNBC) remains a major clinical challenge due to its aggressive behavior and lack of targeted therapies. Accurate early prediction of response to neoadjuvant chemotherapy (NACT) is essential for guiding personalized treatment strategies and improving patient outcomes. In this study, we present an attention-based multiple instance learning (MIL) framework designed to predict pathologic complete response (pCR) directly from pre-treatment hematoxylin and eosin (H&E)-stained biopsy slides. The model was trained on a retrospective in-house cohort of 174 TNBC patients and externally validated on an independent cohort (n = 30). It achieved a mean area under the curve (AUC) of 0.85 during five-fold cross-validation and 0.78 on external testing, demonstrating robust predictive performance and generalizability. To enhance model interpretability, attention maps were spatially co-registered with multiplex immunohistochemistry (mIHC) data stained for PD-L1, CD8+ T cells, and CD163+ macrophages. The attention regions exhibited moderate spatial overlap with immune-enriched areas, with mean Intersection over Union (IoU) scores of 0.47 for PD-L1, 0.45 for CD8+ T cells, and 0.46 for CD163+ macrophages. The presence of these biomarkers in high-attention regions supports their biological relevance to NACT response in TNBC. This not only improves model interpretability but may also inform future efforts to identify clinically actionable histological biomarkers directly from H&E-stained biopsy slides, further supporting the utility of this approach for accurate NACT response prediction and advancing precision oncology in TNBC. Full article

Background: The prognosis management of thyroid cancer remains a significant challenge. This study highlights the critical role of T cells in the tumor microenvironment and aims to improve prognostic precision by integrating bulk RNA-seq and single-cell RNA-seq (scRNA-seq) data, providing a more comprehensive view of tumor biology at the single-cell level. Method: 15 thyroid cancer scRNA-seq samples were analyzed from GEO and 489 patients from TCGA. A multi-level attention graph neural network (MLA-GNN) model was applied to integrate T-cell-related differentially expressed genes (DEGs) for predicting disease-free survival (DFS). Patients were divided into training and validation cohorts in an 8:2 ratio. Result: We systematically characterized the immune microenvironment of metastatic thyroid cancer by using single-cell transcriptomics and identified the important role of T-cell subtypes in the development of thyroid cancer. T-cell-based DEGS between tumor tissues and normal tissues were also identified. Subsequently, T-cell-based risk signatures were selected for establishing a risk model using MLA-GNN. Finally, our MLA-GNN-based model demonstrated an excellent ability to predict the DFS of thyroid cancer patients (1-year AUC: 0.965, 3-years AUC: 0.979, and 5-years AUC: 0.949 in training groups, and 1-year AUC: 0.879, 3-years AUC: 0.804, and 5-years AUC: 0.804 in validation groups). Conclusions: Risk features based on T-cell genes have demonstrated the effectiveness in predicting the prognosis of thyroid cancer. By conducting a comprehensive characterization of T-cell features, we aim to enhance our understanding of the tumor’s response to immunotherapy and uncover new strategies for the treatment of cancer. Full article

Regulatory T cells (Tregs) have multiple roles in the tumor microenvironment (TME), which maintain a balance between autoimmunity and immunosuppression. This research aimed to investigate the interaction between cancer stemness and Regulatory T cells (Tregs) in the breast cancer tumor immune microenvironment. Breast cancer stemness was calculated using one-class logistic regression. Twelve main cell clusters were identified, and the subsequent three subsets of Regulatory T cells with different differentiation states were identified as being closely related to immune regulation and metabolic pathways. A prognostic risk model including MEA1, MTFP1, PASK, PSENEN, PSME2, RCC2, and SH2D2A was generated through the intersection between Regulatory T cell differentiation-related genes and stemness-related genes using LASSO and univariate Cox regression. The patient’s total survival times were predicted and validated with AUC of 0.96 and 0.831 in both training and validation sets, respectively; the immunotherapeutic predication efficacy of prognostic signature was confirmed in four ICI RNA-Seq cohorts. Seven drugs, including Ethinyl Estradiol, Epigallocatechin gallate, Cyclosporine, Gentamicin, Doxorubicin, Ivermectin, and Dronabinol for prognostic signature, were screened through molecular docking and found a synergistic effect among drugs with deep learning. Our prognostic signature potentially paves the way for overcoming immune resistance, and blocking the interaction between cancer stemness and Tregs may be a new approach in the treatment of breast cancer. Full article

Background/Objectives: Freund’s adjuvants induce different immunomodulatory effects, but their underlying molecular mechanisms are unclear. In this study, we investigated whether the immune-stimulating effects of the complete Freund’s adjuvant (CFA) involve the mechanisms of trained immunity (TI). Methods: We examined bone marrow cells (BMCs) isolated from CFA-immunized A/J mice to address this question. Incomplete Freund’s adjuvant (IFA) and Mycobacterium tuberculosis var. bovis Bacillus Calmette-Guérin (BCG) served as negative and positive controls, respectively. We evaluated cytokine profiles, metabolic, and epigenetic changes. Results: First, BMCs from all groups except saline showed varied levels of IL-1β, IL-6, and TNF-α. But expression of CCL5 and CXCL10 was significantly elevated only in the CFA and BCG groups. Transcriptionally, significant elevations were noted for TNF-α and IL-1β in the CFA and BCG groups, whereas CXCL10, IL-6, and IL-10 were upregulated in the CFA and BCG groups, respectively. Second, while BMCs from the BCG group expressed the markers of both the M1 and M2 macrophages, no clear trends were noted in the CFA and IFA groups. Third, cell lysates from the CFA group revealed metabolic reprogramming in the BMCs. Specifically, we observed an increased level of lactate, indicative of aerobic glycolysis, which is implicated in TI, and this was also detected in the IFA group. Fourth, epigenetic analysis revealed histone enrichment in the promoter region of TNF-α, in the CFA group, but to a lesser degree than the BCG group. However, no epigenetic changes were observed in the IFA group. Conclusions: Our data provide new insights into the mechanisms of Freund’s adjuvants and the immunomodulatory effects of CFA could involve the features of TI. Full article

Coronavirus disease 2019 (COVID-19) produced devastating health and economic impacts worldwide. While progress has been made in vaccine development, effective antiviral treatments remain limited, particularly those targeting the papain-like protease (PLpro) of SARS-CoV-2. PLpro plays a key role in viral replication and immune evasion, making it an attractive yet underexplored target for drug repurposing. In this study, we combined machine learning, molecular dynamics, and molecular docking to identify potential PLpro inhibitors in existing drugs. We performed long-timescale molecular dynamics simulations on PLpro–ligand complexes at two known binding sites, followed by structural clustering to capture representative structures. These were used for molecular docking, including a training set of 127 compounds and a library of 1107 FDA-approved drugs. A random forest model, trained on the docking scores of the representative conformations, yielded 76.4% accuracy via leave-one-out cross-validation. Applying the model to the drug library and filtering results based on prediction confidence and the applicability domain, we identified five drugs as promising candidates for repurposing for COVID-19 treatment. Our findings demonstrate the power of integrating computational modeling with machine learning to accelerate drug repurposing against emerging viral targets. Full article

A novel therapeutic approach, inverse vaccination, is being developed to combat autoimmune diseases and other inflammatory conditions. It aims to educate the immune system to recognize self-components as innocuous and stop reacting against them. Inverse vaccination, also referred to as tolerogenic vaccination, introduces autoantigens into the immune system to induce immune tolerance to the nominal antigen. In contrast to conventional vaccination, which aims to train the immune system to identify a pathogen as a potential threat that needs to be eradicated, inverse vaccination is designed to educate the immune system to recognize that an antigen is harmless and, consequently, extinguish the inflammatory attack of the tissues that contain the autoantigen. This article discusses recent progress in using inverse vaccination to design therapeutic interventions in several autoimmune diseases by deprivation of co-stimulatory signaling, tagging autoantigens to trigger immune tolerance in the liver, and mRNA vaccination. Also discussed is a tolerogenic feedback loop implicating B lymphocytes in inverse vaccination. Full article

Objective: Tumor progression is regulated by systemic immune status, nutritional metabolism, and the inflammatory microenvironment. This study aims to investigate inflammatory–nutritional biomarkers associated with metachronous liver metastasis (MLM) in colorectal cancer (CRC) and develop a machine learning model for accurate prediction. Methods: This study enrolled 680 patients with CRC who underwent curative resection, randomly allocated into a training set (n = 477) and a validation set (n = 203) in a 7:3 ratio. Feature selection was performed using Boruta and Lasso algorithms, identifying nine core prognostic factors through variable intersection. Seven machine learning (ML) models were constructed using the training set, with the optimal predictive model selected based on comprehensive evaluation metrics. An interactive visualization tool was developed to interpret the dynamic impact of key features on individual predictions. The partial dependence plots (PDPs) revealed a potential dose–response relationship between inflammatory–nutritional markers and MLM risk. Results: Among 680 patients with CRC, the cumulative incidence of MLM at 6 months postoperatively was 39.1%. Multimodal feature selection identified nine key predictors, including the N stage, vascular invasion, carcinoembryonic antigen (CEA), systemic immune–inflammation index (SII), albumin–bilirubin index (ALBI), differentiation grade, prognostic nutritional index (PNI), fatty liver, and T stage. The gradient boosting machine (GBM) demonstrated the best overall performance (AUROC: 0.916, sensitivity: 0.772, specificity: 0.871). The generalized additive model (GAM)-fitted SHAP analysis established, for the first time, risk thresholds for four continuous variables (CEA > 8.14 μg/L, PNI < 44.46, SII > 856.36, ALBI > −2.67), confirming their significant association with MLM development. Conclusions: This study developed a GBM model incorporating inflammatory-nutritional biomarkers and clinical features to accurately predict MLM in colorectal cancer. Integrated with dynamic visualization tools, the model enables real-time risk stratification via a freely accessible web calculator, guiding individualized surveillance planning and optimizing clinical decision-making for precision postoperative care. Full article

Multiple sclerosis (MS) is a complex autoimmune disease with both genetic and environmental influences, yet its underlying mechanisms remain only partially understood. In this review, we compile evidence suggesting that trained immunity—a form of innate immune memory—may play a crucial role in the autoimmune component of MS. By examining key findings from immunology, neuroinflammation, and MS pathophysiology, we explore how innate immune cells, particularly monocytes and macrophages, could contribute to disease onset and progression through persistent pro-inflammatory responses. Understanding the impact of trained immunity in MS could open new avenues for therapeutic strategies targeting the innate immune system. Full article

Background: Diabetic foot ulcers (DFUs) are a severe complication of diabetes and are characterized by impaired wound healing and a high amputation risk. Exosomes—which are nanovesicles carrying proteins, RNAs, and lipids—mediate intercellular communication in wound microenvironments, yet their biomarker potential in DFUs remains underexplored. Methods: We analyzed transcriptomic data from GSE134431 (13 DFU vs. 8 controls) as a training set and validated findings in GSE80178 (6 DFU vs. 3 controls). A sum of 7901 differentially expressed genes (DEGs) of DFUs were detected and intersected with 125 literature-curated exosome-related genes (ERGs) to yield 51 candidates. This was followed by GO/KEGG analyses and a PPI network construction. Support vector machine–recursive feature elimination (SVM-RFE) and the Boruta random forest algorithm distilled five biomarkers (DIS3L, EXOSC7, SDC1, STX11, SYT17). Expression trends were confirmed in both datasets. Analyses included nomogram construction, functional and correlation analyses, immune infiltration, GSEA, gene co-expression and regulatory network construction, drug prediction, molecular docking, and RT-qPCR validation in clinical samples. Results: A nomogram combining these markers achieved an acceptable calibration (Hosmer–Lemeshow p = 0.0718, MAE = 0.044). Immune cell infiltration (CIBERSORT) revealed associations between biomarker levels and NK cell and neutrophil subsets. Gene set enrichment analysis (GSEA) implicated IL-17 signaling, proteasome function, and microbial infection pathways. A GeneMANIA network highlighted RNA processing and vesicle trafficking. Transcription factor and miRNA predictions uncovered regulatory circuits, and DGIdb-driven drug repurposing followed by molecular docking identified Indatuximab ravtansine and heparin as high-affinity SDC1 binders. Finally, RT-qPCR validation in clinical DFU tissues (n = 5) recapitulated the bioinformatic expression patterns. Conclusions: We present five exosome-associated genes as novel DFU biomarkers with diagnostic potential and mechanistic links to immune modulation and vesicular transport. These findings lay the groundwork for exosome-based diagnostics and therapeutic targeting in DFU management. Full article

Background: Selenium (Se) is a trace mineral element with important roles in enhancing athletic performance and athlete recovery. Objectives: This study aimed to observe the differences in plasma, urinary, erythrocyte, and platelet Se concentrations between sexes and analyze the variations in Se concentrations during the soccer season. The main hypothesis was that significant differences in Se levels would be observed between male and female athletes and that these differences would fluctuate throughout the season due to varying training loads and nutritional factors. Methods: Twenty-two male (20 ± 2 years; 1.76 ± 0.06 m; 14.73 ± 3.13 years’ experience; fifth Spanish division) and twenty-four female soccer players (23 ± 4 years; 1.65 ± 0.06 m; 14.51 ± 4.94 years’ experience; second Spanish division) participated. Three assessments were conducted during the season. Evaluations included anthropometry, body composition, fitness (cardiorespiratory and vertical jump), and nutritional intake. Venous samples of blood and urine were obtained. The concentrations of Se in the plasma, urine, erythrocytes, and platelets were analyzed through inductively coupled plasma mass spectrometry. Results: No differences in Se intake were observed. The Se concentrations in the plasma, urine, and platelets were found to be higher in males, while females showed elevated levels in their erythrocytes (p < 0.05). Throughout the season, plasma and platelet Se concentrations exhibited a progressive increase (p < 0.05). Conclusions: Assessing Se status during the season is essential for evaluating nutritional supplementation to maintain performance given Se’s vital role in the immune and antioxidant systems. Full article