BACKGROUND:Bone marrow microenvironment is an area of interest. Reticulin fibers are a natural component of the microenvironment. Bone marrow fibrosis (BMF) is defined as an increase in reticulin fibers in the bone marrow. In this study, we aimed to explore the frequency of BMF at diagnosis, to investigate differences between BMF and non-BMF patients and the impact of BMF on disease course. METHODS:This single-center retrospective study included patients who had been diagnosed with multiple myeloma (MM) and had been evaluated for BMF between January 2010 and June 2022 with a follow-up period of at least 1 year. RESULTS:A total of 217 patients were subdivided into two groups, namely those with (n = 137) and without BMF (n = 80). The median follow-up time was 44 months (range 12-155). We did not detect any survival difference between patients with and without BMF at diagnosis. However MM with BMF patients had lower hemoglobin values, higher International Staging System (ISS) stages and a tendency towards diffuse plasma cell infiltration and autologous stem cell transplantation (ASCT) was performed less frequently in this patient group. Post-treatment bone marrow biopsies revealed that fibrosis had regressed or completely disappeared in 72% of the patients. CONCLUSION:BMF was observed in 63% of the patients. While fibrosis was not found to be independently associated with survival, diffuse plasma cell infiltration pattern, deeper anemia and higher ISS were found to be related to the fibrosis. Older age and red blood cell (RBC) transfusion need correlated with worsening progression free survival (PFS) and overall survival (OS) in patients with BMF.
Background Given the highly overlapping pancytopenic phenotypes, non-invasive differentiation between aplastic anemia (AA) and myelodysplastic syndromes (MDS) remains a formidable clinical challenge. The current diagnostic gold standard relies on invasive bone marrow biopsy and lacks objective standardization. Here, we develop and externally validate a robust, interpretable machine-learning framework utilizing routine peripheral blood parameters to optimize clinical triage. Methods We retrospectively enrolled patients with histopathologically confirmed AA or MDS, partitioning them into a training set (n = 310) and an internal validation set (n = 131). An independent external cohort (n = 74) was leveraged to evaluate cross-institutional generalizability. Following a rigorous multi-algorithm feature selection framework (incorporating XGBoost, Random Forest, and SVM-RFE), we systematically evaluated 14 machine-learning classifiers. We applied the SHapley Additive exPlanations (SHAP) framework to decode pathophysiological drivers and constructed a visual clinical nomogram for point-of-care application. Results Algorithmic intersection distilled the high-dimensional data into a parsimonious 8-feature panel (CHOL, hsCRP, IL-6, SAA, AGE, LDL-C, HRF, and IL-10). The optimized LightGBM model demonstrated superior discriminative accuracy. Crucially, the model exhibited robust performance in the independent external validation cohort, achieving an area under the curve (AUC) of 0.997 and an overall accuracy of 97.30%. SHAP analysis bridged mathematical predictions with pathophysiology, revealing that advanced age and an "inflammaging" cytokine profile (IL–6, hsCRP, SAA) are the strongest predictive features associated with MDS. Conversely, distinct lipid remodeling (CHOL, LDL–C) and erythropoietic alterations (HRF) shifted the diagnostic probability toward AA. Conclusions In conclusion, we established and externally validated a highly accurate, non-invasive 8-feature LightGBM diagnostic model. Translated into a practical clinical nomogram, this interpretable artificial-intelligence tool serves as an efficient "triage gatekeeper" to minimize unnecessary invasive biopsies, thereby facilitating precision triage in hematology.
Multiple sclerosis (MS) is driven by complex interactions among B cells, autoreactive T cells, and compartmentalized inflammation behind the blood-brain barrier (BBB). Although treatment with B cell-targeting monoclonal antibodies (mAbs) has transformed MS management, their efficacy remains limited by inadequate penetration into the central nervous system (CNS), incomplete depletion of long-lived plasma cells, and development of treatment resistance in a significant number of patients. In recent years, chimeric antigen receptor (CAR)-T cell therapy has emerged as a new therapeutic approach to reset pathogenic immune circuits in MS. CAR-T cell therapy enables more profound and durable depletion of pathogenic B cell populations and, potentially, CNS-associated antibody-producing cells, allowing partial reconstitution of a more tolerant and less autoreactive humoral immune system and reducing intrathecal antibody-mediated inflammation. However, the extent to which CAR-T cells can eradicate deeply compartmentalized CNS-resident immune populations remains under investigation. Nonetheless, CAR-T cell therapy for MS has evolved beyond B cell-depleting strategies, and other types of CAR-T cell therapies with distinct mechanisms of action have also been developed. These strategies include chimeric autoantibody receptor (CAAR)-T cell therapy for selective depletion of autoreactive B cells, CAR-engineered regulatory T (CAR-Treg) cells to restore localized immune tolerance, and T cell receptor mimic CAR-T cells (TCRm CAR-T cells) to recognize autoantigenic peptide-MHC complexes. This review aims to discuss the progress of CAR-T cell therapy for MS and its existing challenges, including safety concerns, optimal antigen selection, and safe access to the CNS. Advances in dual-targeting strategies, chemokine receptor engineering, allogeneic platforms, tailoring strategies to the heterogeneous immunopathology of MS, and in vivo generation of CAR-T cells are also comprehensively discussed.
BACKGROUND AND AIMS:Follicular lymphoma (FL) is an indolent B-cell non-Hodgkin lymphoma characterized by frequent relapses despite initial responsiveness to chemoimmunotherapy. CD19-directed chimeric antigen receptor (CAR) T-cell therapy has shown promising efficacy in relapsed or refractory disease, though safety concerns such as cytokine release syndrome (CRS) and neurological events remain. This study aimed to evaluate the efficacy and safety of CD19 CAR T-cell therapy in relapsed or refractory FL. METHODS:A comprehensive search using six databases, including PubMed, Scopus, and Embase, was performed to identify relevant studies. Data on overall response rate (ORR), complete response rate (CRR), progression-free survival (PFS), duration of response (DOR), overall survival (OS), and adverse events, including CRS and neurological events, were extracted. Pooled estimates were calculated using the quality effects model. This review was registered in PROSPERO (CRD420251133655). RESULTS:Out of 2303 records initially identified, 6 studies met the inclusion criteria. The pooled efficacy analysis demonstrated an ORR of 93.5% (95% CI: 86.3-98.3%) and a CRR of 84.5% (95% CI: 72.6-93.6%). Regarding safety outcomes, the pooled estimate for CRS of any grade was 61.4% (95% CI: 45.0-76.6%). For neurological events, it was 33.6% (95% CI: 13.4-57.1%). Subgroup analyses identified prior therapy lines and CAR construct design as sources of heterogeneity. CONCLUSION:In conclusion, CD19 CAR T-cell therapy demonstrates high efficacy with a generally acceptable safety in relapsed or refractory FL, supporting its consideration for patients with limited treatment options while highlighting the need for further research on long-term outcomes.
A 15-day-old neonate was admitted to a neonatal intensive care unit with peripheral ischemia and a right upper limb abscess following Bacillus Calmette-Guérin (BCG) vaccination. Laboratory findings revealed protein C and S deficiencies with elevated d-dimer levels. Imaging demonstrated a cerebral ischemic lesion secondary to coagulopathy, supporting a diagnosis of purpura fulminans. Initial management included abscess drainage followed by extensive surgical debridement of necrotic tissue, anticoagulation, and partial wound suturing. After the third debridement, the infant received four weekly infusions of 1 × 107 allogeneic adipose-derived mesenchymal stem cells, suspended in 2 mL of saline solution, and administered across 10-15 intralesional and perilesional injection sites. Daily photobiomodulation therapy was used as an adjunctive treatment for analgesia and tissue regeneration. Following the first cell therapy session, progressive reduction in lesion depth and enhanced vascularization were observed, culminating in complete wound closure 70 days after the initial cell therapy session. This approach may support future advances in cell therapy and regenerative medicine for the treatment of difficult-to-heal wounds in neonates and provides a rationale for prospective, randomized, controlled clinical trials.
Leukaemia is a deadly illness that affects a huge number of people all over the world. ALL (Acute lymphocytic Leukaemia) and AML (Acute Myelogenous Leukaemia) are the common type of leukaemia where AML widely affects older people and ALL commonly affects children. Therefore, the projected system employed GMLP-MLDA (Globalized Multi-Layer Perceptron with Modified Linear Discriminant Analysis) for the detection of ALL and AML through the gene expression dataset. Although the LDA is a better system for reducing dimension, it also has limitations, like improper handling of high-dimensional data, lesser samples, and higher feature values. To optimize classical LDA, the proposed system utilized the optimal fisher criterion with covariance. The classification in the projected system was performed by the MLP (Multi-Layer Perceptron) for its capability of handling non-linear complex relationships among the input and output. To enhance the performance of MLP, the projected system used global feature relation samples. The projected system utilized a gene expression dataset that comprises data on the gene of the leukaemia-affected patients and analyses the leukaemia. The performance of the proposed system is compared internally with three classifiers such as DT (Decision Tree), KNN (K-Nearest Neighbour), and XGBoost. Moreover, comparison of the traditional LDA with the modified LDA is performed to expose the efficiency of the proposed dimension reduction system. Significantly, the proposed system has achieved an accuracy of 95% and demonstrates the model's ability to contribute the fields of medical science and molecular biology, with enhanced leukaemia diagnosis for improving the survival rate.
BACKGROUND:The evaluative and prognostic value of liquid biopsy in dendritic cell immunotherapy has not yet been studied, especially with respect to the use of the Wilms tumor gene 1 (WT1) peptide and tumor-specific antigens or neoantigens. OBJECTIVE:We aimed to compare and contrast the genomic responses of these two treatment groups via liquid biopsy. METHODS:This was a real-world retrospective analysis of the liquid biopsy test results of 55 men and 46 women between 4 and 91 years of age who had received DC vaccines pulsed with either neoantigens (n = 45) or WT1 with or without MUC1 antigen (n = 56). RESULTS:The baseline total ctDNA concentration was greater in the neoantigen-pulsed DC vaccine group (p = 0.003). Compared with those in the neoantigen-pulsed vaccine group, the number of tumor mutations (p = 0.025), proportion of high-TMB mutations (p = 0.023), and appearance of new mutations (p < 0.001) in the WT1-pulsed vaccine group were significantly greater. Additionally, the relationships between the number of mutations and duration of treatment were significantly different between the two groups (p = 0.016). CONCLUSIONS:Genomic responses were significantly different between the two treatment groups. Preexisting tumor genetics can affect the prognosis of personalized DC immunotherapy, highlighting the role of liquid biopsy in precision oncology.
INTRODUCTION:The success of allogeneic cell therapies depends on high-quality starting material derived from healthy donors. However, variability in donor characteristics and collection procedures such as leukapheresis can impact cell yield and product quality. The aim of this study was to identify donor‑ and procedure‑related factors associated with leukopak yield and composition, and to assess the stability of immune‑cell subsets in repeat donors. METHODS:This was a retrospective analysis of 184 leukopak donations from 104 healthy donors across three German centers (2021-2024). Donor demographics, pre-collection complete blood count (CBC) values, and leukapheresis parameters were evaluated to identify predictors of white blood cell (WBC) yield and leukopak composition. Intra-donor variability was assessed in repeat donors. RESULTS:Donors were predominantly male (96.2%) with a mean age of 36 years and body mass index (BMI) of 30.0 kg/m². The average WBC yield per leukopak was 15.9 × 10⁹ cells, with high viability (99.1%). WBC yield showed positive correlations with BMI (r = 0.2751, p = 0.0001), processed blood volume (r = 0.2673, p = 0.0002), and pre-collection CBC values (r = 0.3939, p < 0.0001). Natural killer cell frequency increased significantly with age, while CD3+ T cells declined (both p < 0.0001). Repeat donors demonstrated consistent immune cell profiles over time, despite some variability in total WBC yield. CONCLUSIONS:This study identified key donor characteristics that influence leukopak collection efficiency, particularly BMI, age, and pre-collection CBC values. Understanding these demographic factors enables better management of leukopak variability, leading to more predictable, consistent, and efficient starting materials for cell therapy manufacturing.
Risk assessment in AML is mainly based on cytogenetic and molecular classification. Even with these systems, induction failure and early death remain common, especially in older patients. In clinical practice, patients often do poorly when aggressive leukemia biology and patient frailty occur together. We aimed to better define these high-risk patterns by identifying specific combinations of clinical and genomic factors linked to early treatment failure. We analyzed 942 specimens out of 805 patients from the BeatAML2 cohort and applied fault tree analysis (FTA), a structured method that identifies small groups of factors that, when present together, are associated with clearly higher event rates. Separate models were built for induction failure and 60-day mortality using gene mutations and routine clinical variables available at diagnosis. RUNX1 and TP53 mutations were strongly associated with failure to achieve remission, particularly when combined with low platelet counts. Early mortality was more closely linked to patient-related factors, especially advanced age together with low albumin or impaired kidney function. We defined an extreme-risk constellation (XRC_ANY) as the presence of at least one high-risk combination. This was observed in 32.6% of patients and was associated with significantly shorter overall survival (median 297 vs 861 days, p<0.0001). In multivariable Cox analysis, XRC_ANY remained independently associated with survival after adjustment for cytogenetic-molecular risk classification (hazard ratio 2.03, p<0.0001). Overall, these findings suggest that poor outcomes in AML often occur when aggressive leukemia and patient frailty are present at the same time. Fault tree analysis may help refine risk assessment within existing classification systems.
BACKGROUND:Hematological malignancies, particularly multiple myeloma (MM) and lymphomas, pose major clinical challenges due to their biological complexity and inter-patient heterogeneity. Although diagnostic and therapeutic approaches have evolved, significant gaps remain in the integration of multi-omics data, risk stratification, and treatment response prediction. METHODS:This state-of-the-art review examines recent developments in artificial intelligence (AI) applied to MM and lymphomas. A systematic literature search was conducted in PubMed, Web of Science, Scopus, and specialized journals including the Journal of Hematology & Oncology, Leukemia, and Blood for studies published between 2018 and 2024. After screening and full-text assessment, 50 studies met the inclusion criteria following PRISMA guidelines. Studies were selected based on methodological rigor and clinical relevance. RESULTS:AI models demonstrate robust capabilities across diagnostic, prognostic, and therapeutic applications. Single-center studies report outstanding metrics, including AUCs up to 0.99 for myeloma lesion classification, while multicenter validation yields more conservative yet robust metrics. In both diseases, multimodal approaches consistently outperform unimodal models across all clinical applications. Despite these advances, key challenges in data diversity, technical heterogeneity, and model interpretability remain under active investigation. CONCLUSIONS:AI shows transformative potential for MM and lymphoma management, particularly through multimodal integration. Bridging the gap with clinical practice requires transparency, computational efficiency, and ethically grounded validation. In addition, close collaboration among clinicians, data scientists, and institutions is essential. These combined efforts are key to establishing AI as a reliable tool in everyday hematology.
This review examines the potential application of nanogels as novel drug carriers for RNA-based therapy in the treatment of skin cancer. Relevant information was obtained from reputable scientific databases, including peer-reviewed articles. Nanogels, characterized by their nanoscale size and three-dimensional network structure, offer superior pharmacokinetic properties for drug delivery, including enhanced skin permeation, reduced burst release, and minimized systemic side effects. RNA molecules are very sensitive and can easily be degraded; therefore, by encapsulating them in nanogels, their stability, uptake, and delivery to skin cancer cells can be enhanced. This review elaborates on the molecular pathways MAPK/ERK/ PI3K/AKT/mTOR/p53/Hedgehog/NF-κB Pathways in skin cancers and the techniques for loading RNA into nanogels, such as electrostatic interactions, polymer-drug attachment, and LBL deposition. It also explains the mode of action of these nanogel-based drug delivery systems, which includes cellular uptake, intracellular targeting, RNA delivery, and gene silencing mechanisms. The issues of interest and concern in the context of large-scale production and clinical utilization of RNA-loaded nanogels are discussed. The current review addresses the existing gap by integrating pathway-level mechanistic insights in skin cancer with recent advances in nanogel-mediated RNA delivery, highlighting how these systems may overcome current limitations of conventional therapies and enable more precise and effective treatment strategies.
No recognized standard of care exists at the present time for treatment for large B-cell non-Hodgkin lymphoma that relapses or progresses after chimeric antigen receptor T-cell (CAR T-cell) therapy. Bispecific T-cell engagers (BiTEs) have emerged as an effective treatment for patients with relapsed or refractory (R/R) LBCL. The purpose of this systematic review and meta-analysis is to appraise the totality of evidence on the role of BiTEs as treatment for LBCL after CAR T-cell therapy failure. Results showed that pooled overall response rate of all BiTEs is 51 %, and the pooled complete remission rate is 31 %. The pooled rates for progression-free and overall survival are 31 % and 48 %, respectively. The pooled non-relapse mortality rate is 4 %, emphasizing the relative safety of BiTEs in this population. Available data did not permit a comparative assessment across different BiTE products. Therefore, future research, specifically prospective head-to-head randomized controlled trials, would be necessary to make definitive comparisons.
High bone mineral density (BMD) is common and sometimes an incidental finding. The causes are numerous. Among them, none has previously been attributed to total body irradiation (TBI). We present the case of a 56-year-old female patient with a history of T-lymphoblastic lymphoma at age 33 who was treated with allogeneic hematopoietic stem cell transplantation following a conditioning regimen including a single-fraction 10 Gray TBI. This patient was in complete remission but experienced several transplant-related late effects. She presented to the rheumatology outpatient clinic with chronic mechanical low back pain and a history of early menopause. Bone assessment by densitometry revealed high bone mineral density with a lumbar spine L2-L4 T-score of +6.3 standard deviation (SD) (1.939 g/cm²), right femoral neck T-score of +7.2 SD (1.849 g/cm²), right total femur T-score of +4 SD (1.484 g/cm²), distal radioulnar T-score of +0.7 SD (0.495 g/cm²). Imaging revealed sclerotic lesions in the vertebrae, femoral cortices and pelvis. An etiological workup excluded other causes such as fluorosis, mastocytosis, renal osteodystrophy, hypoparathyroidism/pseudohypoparathyroidism and myelofibrosis. Bone growth factors and resorption markers were normal. Genetic sequencing showed no significant abnormalities. Based on this comprehensive evaluation, TBI was identified as a possible contributing factor to the occurrence of high BMD. The patient was managed with analgesics and regular follow-up. This case highlights the importance of a systematic etiologic approach to high bone mineral density and underscores the need for future scientific research to better understand this phenomenon for which the pathological relationship with radiation exposure remains unknown.
Allogeneic hematopoietic stem cell transplantation (allo-HSCT) remains the only curative treatment for relapsed/refractory acute myeloid leukemia (r/rAML), high-risk myelodysplastic syndromes (MDS), and myeloproliferative neoplasms (MPN). Sequential conditioning, combining cytoreductive chemotherapy with reduced-intensity conditioning, was designed to reduce toxicity while preserving efficacy in frail patients. While numerous studies have evaluated different sequential regimens, the impact of rest period duration between the two phases remains unexplored. This bicentric retrospective study analyzed 82 allo-HSCT between 2013 and 2021, in patients with high-risk myeloid malignancies (median age 58 years). Short-bridge-to-transplant regimens (SBTT, n=44) with rest periods of 7 days or less, including the well-known FLAMSA-RIC, were compared to long-bridge-to-transplant regimens (LBTT, n=38) with rest periods longer than 7 days. After a median follow-up of 33 months, rest period duration did not significantly affect progression-free survival (PFS), overall survival, relapse incidence or non-relapse mortality (NRM). Two-year PFS was 35.1% for SBTT versus 57.2% for LBTT (aHR 1.62; P=0.13). However, measurable residual disease-free survival (MRD-FS) was significantly improved with LBTT (aHR 2.15; P=0.02). Despite longer median aplasia, LBTT showed comparable complications and enabled more intensive chemotherapy without increased NRM. LBTT appears feasible and not inferior to SBTT, with a signal of improved MRD control that may reflect better temporal separation and management of treatment-related toxicities, safe delivery of higher-dose or more intensive chemotherapy, and potential leukemic cell-cycle synchronization effects. While center-specific practices and regimen heterogeneity limit definitive conclusions, these hypothesis-generating findings highlight an underexplored dimension of sequential conditioning and warrant further investigation in larger prospective studies.
Background : Chronic Myeloid Leukemia (CML) progresses through chronic, accelerated, and blast crisis phases, posing challenges for disease stratification and predicting therapeutic response. IGF2BP3 (Insulin-like Growth Factor 2 mRNA Binding Protein 3) has recently gained attention as a potential prognostic biomarker due to its role in RNA stabilization and oncogenic signaling. Methods : This study employed a multi-platform approach, utilizing immunohistochemistry (IHC), ELISA, qRT-PCR, and Western blotting to assess IGF2BP3 expression in 121 CML patient samples across various disease phases. Statistical modeling (R-Studio) followed by Advanced artificial intelligence (ChatGPT 4.0) was employed to correlate IGF2BP3 expression with clinical parameters and therapeutic response outcomes. Results : IGF2BP3 expression showed a stepwise increase from the chronic to blast crisis phase, correlating with disease severity and therapeutic non-responsiveness. Both IHC staining intensity and serum IGF2BP3 levels were highest in blast crisis patients, findings further validated by qRT-PCR and Western blot analyses. Statistical model (Chat GPT & R-Studio) based regression modeling confirmed a strong association between IGF2BP3 levels, P210 translocation percentage, and blast count. Notably, patients who were non-responsive to therapy exhibited significantly elevated IGF2BP3 expression compared to responders. Conclusions : IGF2BP3 serves as a robust biomarker for disease progression and therapeutic resistance in CML. Elevated IGF2BP3 expression may identify patients at higher risk of poor treatment response and relapse, making it valuable for risk stratification and longitudinal disease monitoring. While this study does not address therapeutic targeting of IGF2BP3, its strong association with resistance phenotypes supports further exploration of IGF2BP3 as a predictive biomarker in precision hematologic oncology.
The CXCR4/CXCL12 signaling axis plays a central role in regulating immune cell trafficking, hematopoietic homeostasis, and organogenesis. However, dysregulation of this axis contributes to the pathogenesis of numerous disorders, highlighting CXCR4 inhibition as a promising therapeutic strategy. Mavorixafor, the first orally available small-molecule CXCR4 antagonist, recently received FDA approval for WHIM syndrome (Warts, Hypogammaglobulinemia, Infections, and Myelokathexis) and is currently being developed for additional indications. Despite extensive research on CXCR4 biology, a comprehensive analysis of mavorixafor’s pharmacologic profiles and its performance in preclinical and clinical settings is lacking. This systematic review synthesizes the pharmacology, efficacy, and safety of mavorixafor, summarizing evidence from various sources, including PubMed/MEDLINE, Web of Science, Google Scholar, conference proceedings, clinicaltrials.gov, and FDA resources. Mavorixafor demonstrates potent CXCR4 antagonism, rapid oral absorption, and a long half-life, enabling once-daily dosing. Clinically, it has been shown to increase neutrophil counts and reduce infection rates, contributing to its approval for WHIM syndrome. Early clinical studies in chronic neutropenia indicate sustained neutrophil elevation and decreased dependence on G-CSF. Additionally, emerging data suggest potential benefits in specific malignancies and its utility in mobilizing hematopoietic stem and progenitor cells, as well as in other immune-mediated disorders related to CXCR4 dysregulation. Furthermore, this review positions mavorixafor within the broader CXCR4-targeted therapeutic landscape, identifying current research gaps and suggesting directions for future studies. In conclusion, by integrating mechanistic insights with preclinical and clinical findings, this article highlights mavorixafor’s promise as a targeted therapy with the potential to transform treatment paradigms for CXCR4-driven diseases.
Objective Hepatocellular carcinoma (HCC) is a heterogeneous malignancy with poor prognosis. This study identifies metabolism-related genes (MRGs) associated with HCC prognosis, develops a multi-gene prognostic model based on metabolic reprogramming and immune escape, and evaluates their roles in the tumor microenvironment (TME) to guide diagnosis and treatment. Methods Transcriptomic and clinical data from HCC patients were analyzed using public databases (TCGA). MRGs linked to HCC staging and prognosis were identified. Weighted gene co-expression network analysis (WGCNA) detected metabolic gene modules associated with tumor progression. A multi-gene prognostic model was built using LASSO and random survival forests (RSF). Model performance was evaluated with Kaplan-Meier analysis, ROC curves, and Nomogram. Single-cell analyses explored metabolic interactions, and enrichment and mutation analyses assessed key genes' significance. PCR validated gene expression. Results A total of 374 metabolism-related genes were linked to HCC staging. A prognostic model with eight key genes (UCK2, CAD, NUDT1, PIGU, IVD, CAT, ALDH6A1, SLC2A2) showed strong predictive performance across TCGA, ICGC, and GEO cohorts. Low-risk patients had significantly better survival (5-year survival prediction AUC of 0.75). PCR validation confirmed differential expression: UCK2, CAD, NUDT1, and PIGU were upregulated, while IVD, CAT, ALDH6A1, and SLC2A2 were downregulated. Immune infiltration analysis indicated an accumulation of immunosuppressive cells in the high-risk group, whereas the low-risk group exhibited an immune-active phenotype characterized by elevated infiltration of effector cells. Single-cell analysis uncovered metabolic-immune interactions in the TME. Gene mutation analysis showed frequent mutations in the high-risk group, linked to invasiveness and treatment resistance. Conclusion This study identifies key metabolism-related genes linked to HCC prognosis and develops a multi-gene prognostic model. Our findings highlight the roles of metabolic reprogramming and immune escape in HCC, providing a foundation for future immune and metabolic interventions.
Artificial intelligence (AI) has emerged as an exemplified tool in the field of modern biomedical technology. The unprecedent COVID-19 pandemic and other infectious disease crises have highlighted the critical need for rapid and accurate vaccine development processes. The traditional method of vaccine development methods are often time-consuming, costly, and inefficient. On contrary to this, the AI streamlines vaccine development from antigen prediction to clinical trial optimization by integrating computational biology, machine learning, structural bioinformatics, and immunoinformatic. AI has many potential applications in vaccine research, and this review covers all of the bases, from the fundamentals of AI in biology to immunogen design, clinical trial data mining, efficacy prediction modelling modelling, and adjuvant optimization. The review also investigates potential unknown issues, ethical concerns, and future developments in AI-driven vaccine development. This paper emphasizes the potential of AI to transform global preparedness against infectious diseases by combining evidence from various disciplines in vaccine development.
