Search Results (931)

Belatacept, a selective costimulation blocker targeting the CD28–CD80/86 pathway, represents a major innovation in solid organ transplantation immunosuppression. By providing upstream inhibition of T-cell activation without calcineurin inhibition, belatacept offers the potential for improved long-term graft and patient outcomes with reduced nephrotoxicity and metabolic adverse effects. This review summarizes the mechanistic rationale, pivotal evidence, and clinical experience supporting the use of belatacept as first-line or conversion therapy in solid organ transplantation, while addressing safety, pharmacoeconomic impact, and future research directions. A comprehensive analysis of pivotal phase II–III trials (BENEFIT, BENEFIT-EXT), recent prospective conversion studies, and ongoing trials in liver, heart, and lung transplantation was performed. Safety data and health–economic evaluations were critically appraised. In kidney transplantation, belatacept-based immunosuppression provides superior renal function and improved metabolic profiles compared with calcineurin inhibitors (CNIs), though with higher early acute rejection rates. In liver, heart, and lung transplantation, evidence remains limited, with de novo use contraindicated in liver grafts due to excess mortality and rejection. Conversion from CNI to belatacept in selected patients improves renal outcomes without compromising graft survival. Safety considerations include a higher risk of post-transplant lymphoproliferative disorder (PTLD) in Epstein–Barr virus-negative recipients. Belatacept represents a paradigm shift in transplant immunology by targeting upstream T-cell activation. While currently approved only for kidney transplantation, ongoing studies in thoracic and hepatic grafts may expand its therapeutic role. Personalized patient selection, combination regimens mitigating rejection risk, and real-world cost-effectiveness analyses will define its place in future precision immunosuppression strategies. Full article

Cancer patients are highly vulnerable to severe COVID-19, requiring models that capture tumor–virus interactions. We investigated tumor- and variant-specific effects of SARS-CoV-2 Gamma and Delta infections using patient-derived organoids (PDOs) from metastatic breast, lung, and colorectal cancers. Viral infection was quantified by Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR) 24 h post-infection, and morphological changes and immune mediators were profiled. Genomic analysis using whole-exome sequencing was performed to identify contributing host-related gene alterations. The Delta variant produced consistently higher viral loads in lung and breast PDOs, while colorectal PDOs showed variable susceptibility. Infection led to reduced area and perimeter and increased circularity across all tumor types. Immune profiling revealed distinct responses: Gamma decreased Interferon alpha (IFNα) in lung PDOs and increased E-selectin in colorectal PDOs. Delta broadly reduced inflammatory mediators in lung [10 kDa interferon gamma-induced protein (IP-10) and Intercellular adhesion molecule 1 (ICAM-1)] and breast [Interleukin-6 (IL-6), Interleukin-13 (IL-13), and Interleukin-17A (IL-17A)] PDOs, while increasing Macrophage inflammatory protein 1-beta (MIP-1β) in colorectal PDOs. Host gene variants involved in trafficking (FYCO1 and RAB7A) and immune signaling (FOXA2, SFTPD, STAT3, and TET2) were associated with differential infection profiles. These findings show that SARS-CoV-2 induces variant- and tumor-specific morphological and immunological changes in cancer PDOs, highlighting the potential of this model to unravel host–virus interactions and identify genetic factors that shape infection outcomes in cancer. Full article

Background/Objectives: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
infection in children presents with a heterogeneous clinical spectrum, whereas
multisystem inflammatory syndrome in children (MIS-C) is a distinct immunological
entity characterized by a hyperinflammatory phenotype and a distinct biological
architecture. Identifying routine biomarkers with early discriminatory utility is essential
for rapid differentiation between MIS-C and coronavirus disease 2019 (COVID-19).
Methods: We conducted a retrospective comparative study of 144 pediatric patients with
COVID-19 or MIS-C admitted to a single specialized medical center. The analyses
integrated classical statistical methods, Benjamini–Hochberg false discovery rate
correction (FDR), penalized regression models, and machine learning algorithms to
identify biomarkers with discriminative value, using only routine laboratory tests.
Results: MIS-C was associated with an intense inflammatory profile, characterized by
increases in C-reactive protein (CRP), neutrophil-to-lymphocyte ratio (NLR), and plateletto-
lymphocyte ratio (PLR), lymphopenia, and selective electrolyte disturbances,
highlighting a coherent biological architecture. In contrast, COVID-19 showed limited
associations with traditional inflammatory markers. Predictive models identified a stable
core of biomarkers with excellent performance in Random Forest analysis (area under the
curve, AUC = 0.95), and reproducible thresholds (CRP ~3.7 mg/dL, NLR ~3.3, PLR ~376;
potassium ~4.2 mmol/L). These findings were independently confirmed using penalized
Ridge regression, where the reduced model achieved superior discrimination compared
to the full 13-variable model (AUC = 0.93 vs. 0.89) and maintained stable performance
under internal cross-validation, reinforcing the clinical relevance of this compact
biomarker panel. Conclusions: MIS-C is clearly distinguished from COVID-19 by a
specific and reproducible immunological signature. The identified biomarkers may
represent a potential foundation for the development of simple clinical algorithms for
pediatric triage and risk stratification, opening the prospect of a simplified scoring tool
applicable in emergency settings. Full article

Chagas disease (CD), caused by Trypanosoma cruzi, is a neglected tropical illness affecting 6–8 million people in Latin America. Reaching scholarly consensus on the host response to T. cruzi infection remains a significant challenge, primarily due to substantial heterogeneity in outcomes driven by both the choice of animal model and the infecting parasite’s discrete typing unit (DTU). This variability complicates the evaluation and comparison of new therapeutic compounds against existing drugs, namely benznidazole and nifurtimox. This study provides a comprehensive, kinetic, multifaceted characterization of the acute infection using the highly virulent T. cruzi Y strain (TcII) in outbred Swiss mice. Here, crucial infection parameters are presented, including the optimal infective dose, the parasitemia dynamics, tissue damage markers, hematological profiles, cytokine production (Th1/Th2/Th17/Th22), and molecular parasite identification in target organs (heart, colon, esophagus, spleen, and liver) across the span of the infection. The novelty of this study lies in the kinetic integration of these parameters within a defined model; rather than presenting isolated data points, we demonstrate how the biochemical, physiological, and clinical signs and immunological responses, with the resulting organ involvement, evolve and interact over time. To complete the report, a necropsy evaluation was performed at the end of the acute, fatal infection, and it is presented here. This study fulfills a long-standing recommendation from diverse drug discovery groups for the creation of a definitive reference model to standardize preclinical testing for anti-Chagasic agents. Full article

Mycoplasma pneumoniae pneumonia (MPP) is a common respiratory infection characterized by significant inflammatory responses and lung tissue injury. However, the precise immunological mechanisms and temporal dynamics of key cytokines driving pulmonary inflammation in MPP are still unclear. This study aimed to investigate the underlying immunological mechanisms and cytokine dynamics in MPP. We established an acute MPP murine model via intranasal administration of M. pneumoniae. This model recapitulates key features of human MPP, such as robust airway inflammation and cytokine production. Comprehensive analyses were conducted, including histopathology, flow cytometry, and cytokine profiling. Results showed severe inflammatory responses with prominent infiltration of neutrophils and macrophages in lung tissue, whereas monocyte populations were significantly reduced, indicating a shift towards myeloid cell predominance. Notably, 36 cytokines, including pro-inflammatory interleukins (IL-1β, IL-6, IL-17A) and chemokines, were statistically significantly upregulated in bronchoalveolar lavage fluid compared to the normal group, highlighting a cytokine storm associated with lung inflammation and tissue damage. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway analysis further revealed enriched pathways related to cytokine-cytokine receptor interactions and IL-17 signaling, suggesting potential therapeutic targets. In conclusion, this study preclinical provides insights into the innate immune response and cytokine-driven pathology in acute MPP, underscoring the pivotal roles of myeloid cells and pro-inflammatory cytokines. Future research should focus on clinical validation of these findings to assess their translational potential and the exploration of immunomodulatory strategies informed by this model to mitigate MPP severity. Full article

Background: Despite significant advances in genetics, immunology, and endometrial research, the underlying cause of nearly half of recurrent pregnancy loss (RPL) cases remains unknown. This highlights the limitations of conventional diagnostic approaches and underscores the need for methods that can detect complex, subtle biological patterns. Objectives: To summarize and critically assess how artificial intelligence (AI) is changing our knowledge of, ability to predict, and future therapeutic management of RPL, with a focus on machine learning (ML) approaches that identify latent biological pathways and multifactorial contributors to pregnancy loss. Methods: This narrative review summarizes contemporary research on AI applications in reproductive medicine. Research using imaging, proteomic, genomic, clinical, and multi-omics information to create predictive or mechanistic models associated with RPL provided evidence. Results: AI-based approaches are increasingly demonstrating the ability to detect complex interactions among environmental, immunological, biochemical, and genetic factors associated with RPL. ML and deep learning (DL) models enhance prognostic accuracy, identify novel candidate biomarkers, and provide insights into the systemic and molecular mechanisms underlying pregnancy loss. Integrating heterogeneous data through AI supports the development of personalized reproductive profiles and can improve prediction and counseling. Conclusions: AI has the potential to improve both personalized prediction and mechanistic understanding of RPL. However, clinical translation is currently hampered by a number of important issues, including small and diverse datasets, conflicting diagnostic definitions, limited external validation, and a lack of prospective clinical trials. To responsibly integrate AI tools into reproductive care, these limitations must be addressed. Full article

Dendritic cells (DCs) are essential for antiviral immunity but are also susceptible to HIV-1 infection. Although sensing and restriction pathways in DCs are well described, the mechanisms underlying latent infection and its functional consequences remain unclear. In this study, we performed transcriptomic profiling of monocyte-derived DCs harboring transcriptionally active (Active-HIV) or latent HIV-1 (Latent-HIV) proviruses using a dual-reporter virus. Gene set enrichment analysis revealed suppression of metabolic and stress-modulatory programs in Active-HIV compared to unexposed DCs. In contrast, Latent-HIV showed broad downregulation of pathways, including interferon and innate responses and metabolic programs, indicating a hyporesponsive and dampened antiviral state despite the absence of differentially expressed genes (DEGs). DEG analysis of Active-HIV versus Latent-HIV showed that active transcription associates with cellular stress, cytoskeletal remodeling, and RNA processing. Functional analyses further demonstrated the activation of RNA processes, the suppression of antigen-presentation pathways, and altered membrane and cytoskeletal signaling in Active-HIV. These pathways suggest that transcriptionally active HIV-1 is linked to cellular programs supporting replication, coinciding with a metabolically strained yet immunologically engaged state that may impair antigen presentation. Conversely, latently infected DCs display a hyporesponsive state consistent with proviral silencing. This dichotomy reveals distinct mechanisms of DC dysfunction that may facilitate HIV-1 persistence and immune evasion. Full article

Infectious diseases remain a persistent global health threat, intensified by the rapid emergence of antibiotic-resistant pathogens. Despite the transformative impact of antibiotics, the escalating resistance crisis underscores the urgent need for alternative therapeutic approaches. Antimicrobial peptides (AMPs) have emerged as promising candidates due to their broad-spectrum antimicrobial and immunomodulatory activities. The present study investigated 82 honey bee antimicrobial peptides (BAMPs) representing seven families: abaecin, apamin, apisimin, apidaecin, defensin, hymenoptaecin, and melittin among eight honey bee species. Immunoinformatics analyses identified five peptides (P15450, A0A2A3EK62, Q86BU7, C7AHW3, and I3RJI9A) with high antigenicity and non-allergenic profiles. Structural modeling, molecular docking with TLR3 and TLR4-MD2, and molecular dynamics simulations revealed stable receptor-peptide interactions and favorable binding energetics, further supported by silico immune simulations. Overall, these findings suggest that the selected BAMPs exhibit strong immunogenic potential and may serve as effective adjuvants or carrier molecules in subunit vaccine design against drug-resistant pathogens; however, further experimental validation is essential to confirm their safety and immunological efficacy. Full article

Autoantibodies have long been regarded as passive reflections of immune dysregulation in connective tissue diseases (CTDs). Recent advances in systems immunology and molecular pathology have fundamentally redefined them as active molecular fingerprints that delineate distinct disease endophenotypes with predictive power for clinical trajectories and therapeutic responses. Rather than mere epiphenomena, autoantibodies encode precise information about dominant immune pathways, organ tropism, and pathogenic mechanisms. This review synthesizes emerging evidence that autoantibody repertoires—defined by specificity, structural properties, and functional characteristics—stratify patients beyond traditional clinical taxonomy into discrete pathobiological subsets. Specific signatures such as anti-MDA5 in rapidly progressive interstitial lung disease, anti-RNA polymerase III in scleroderma renal crisis, and anti-Ro52/TRIM21 in systemic overlap syndromes illustrate how serological profiles predict outcomes with remarkable precision. Mechanistically, autoantibody pathogenicity is modulated by immunoglobulin isotype distribution, Fc glycosylation patterns, and tissue-specific receptor expression—variables that determine whether an antibody functions as a biomarker or pathogenic effector. The structural heterogeneity of autoantibodies, shaped by cytokine microenvironments and B-cell subset imprinting, creates a dynamic continuum between pro-inflammatory and regulatory states. The integration of serological, transcriptomic, and imaging data establishes a precision medicine framework: autoantibodies function simultaneously as disease classifiers and therapeutic guides. This endophenotype-driven approach is already influencing trial design and patient stratification in systemic lupus erythematosus, systemic sclerosis, and inflammatory myopathies, and is reshaping both clinical practice and scientific taxonomy in CTDs. Recognizing autoantibodies as endophenotypic determinants aligns disease classification with pathogenic mechanism and supports the transition towards immunologically informed therapeutic strategies. Full article

Background: Adrenocortical carcinoma (ACC) is a rare, aggressive malignancy where endogenous steroid excess may foster immune evasion. However, whether this hormonal axis directly modulates the antigen presentation machinery remains unclear. Methods: We applied an immunoinformatics approach to the TCGA-ACC cohort (n = 79) to investigate relationships among steroid phenotype, HLA expression, tumor microenvironment (TME), and patient outcome. Key findings were assessed in an independent validation cohort (ENSAT-ACC, n = 44) using C1A/C1B molecular subtypes corresponding to the steroid phenotypes. Results: Stratification by steroid phenotype revealed two distinct immunological profiles. The high steroid production (HSP) phenotype was associated with suppressed HLA expression and a lymphocyte-depleted “cold” TME. In contrast, the low steroid production (LSP) phenotype displayed elevated HLA expression, enriched T-cell infiltration, and upregulation of immune checkpoints (e.g., PDCD1, CTLA4), consistent with an inflamed but exhausted TME. The core signature of HLA downregulation in the HSP-like phenotype (C1A) and the significant survival advantage of the LSP-like phenotype (C1B) were confirmed in the validation cohort, demonstrating biological robustness despite platform and sample size differences. Conclusions: These findings identify the steroid phenotype as a critical regulator of immune escape in ACC. Our results support incorporating this stratification as a biomarker for patient selection, identifying LSP tumors as the subgroup most likely to benefit from immune checkpoint blockade due to their “hot” yet exhausted microenvironment. Full article

Background: In melanoma diagnostics key molecular markers, such as BRAF, NRAS, and KIT mutations also paved the way for targeted therapies. Immunotherapies, including immune checkpoint inhibitors like anti-CTLA-4 and anti-PD-1/PD-L1, have revolutionized treatment, improving survival outcomes for advanced-stage melanoma patients. Despite these advances, challenges such as resistance to targeted therapies and variability in patient responses to immunotherapy remain critical issues. The purpose of the project is to characterize the molecular landscape of a set of 28 malignant melanomas using next-generation sequencing, identify the prevalence and nature of class 3–5 variants (e.g., NRAS, BRAF, KIT, TP53), assess the genetic complexity and molecular patterns, and use these insights to inform personalized therapies and optimize patient stratification for potential combination strategies (targeted therapy followed by immunotherapy). Methods: We analyzed a set of malignant melanoma of the skin of 17 women (61%) and 11 men (39%) at the age of 23 to 85 years (median: 63 years) by tumor-only next generation sequencing. Results: 22/28 cases (79%) present a pathogenic or likely pathogenic variant with an allelic frequency of ≥5%. In total 42 distinct somatic pathogenic or likely pathogenic variants with an allelic frequency of ≥5% could be detected. The most frequent pathogenic molecular alteration in these melanomas were found in NRAS (25%) and BRAF (25%). The most frequent molecular alteration of unknown significance was found in FANDC2 (46%), NOTCH3 (39%), ARID1A (32%), PMS2 (32%), POLE (29%), NOTCH1 (29%), TSC2 (25%), SMARCA4 (25%), ATR (25%) and TERT (21%). Conclusions: While NRAS and BRAF were the most frequent actionable alterations (each 25%), a broad spectrum of variants of unknown significance (e.g., FANDC2, NOTCH3, ARID1A, PMS2, POLE, NOTCH1, TSC2, SMARCA4, ATR and TERT) also predominates, underscoring the genetic complexity of melanoma. These variants complicate clinical decision-making because their contribution to tumorigenesis, therapeutic response, and prognosis remains uncertain. Nevertheless, these variants also offer a valuable resource for future research, as they may uncover novel pathogenic mechanisms or therapeutic targets once their significance is elucidated. Integrating comprehensive genetic profiling with immunologic markers can enhance patient stratification and support rational, potentially synergistic strategies, such as combining targeted therapies with immunotherapy, to optimize clinical outcomes. This study is limited due to a small cohort and limited available clinical data. Larger cohort studies and prospective clinical trials are necessary to validate and explore the interplay between molecular and immune biomarkers as well as general biological mechanism in paving therapeutic way in melanoma. Full article

Background/Objectives: This study examined the metabolic, oxidative, immunological, and histomorphological effects of the multicomponent fermented biological product derived from camel milk, Inullact-Fito, in comparison to metformin in a rat model of alloxan-induced diabetes resulting from insulin insufficiency. The model was chosen as an experimental system that replicates pancreatic β-cell damage induced by oxidative stress rather than insulin resistance. Methods: Alloxan-induced diabetes was used to evaluate metabolic, oxidative, immunological, and histomorphological alterations. Metformin was utilized as a pharmacological comparator. Blood glucose levels, circulating insulin concentrations, markers of oxidative stress and lipid peroxidation, immunoglobulin levels, CD4⁺/CD8⁺ T cell balance, and pancreatic histostructure were assessed. Results: Alloxan administration led to substantial hyperglycemia, oxidative stress, immunological imbalance, and structural damage to pancreatic tissue. Following therapy with Inullact-Fito, blood glucose levels reduced dramatically (from 21.9 ± 0.22 to 9.85 ± 0.10 mmol/L, p < 0.05), circulating insulin concentrations were largely corrected, oxidative stress and lipid peroxidation markers decreased. Immunological evaluation revealed decreased serum immunoglobulin M and IgG levels (p < 0.05) and partial normalization of the CD4⁺/CD8⁺ T cell balance. Metformin showed comparative effects; however, its activity in this model is limited by its primary mechanism related to insulin resistance. Conclusions: Overall, the data reveal that Inullact-Fito combines metabolic, antioxidant, and immunomodulatory actions under experimental oxidative and metabolic stress conditions. Further research using models of insulin resistance and type 2 diabetes, as well as long-term clinical trials, is needed to fully evaluate the therapeutic potential, safety profile, and translational importance of this fermented dairy product as a functional nutritional intervention. Full article

Background: Advances in molecular pathology have transformed NSCLC (Non-Small Cell Lung Cancer) diagnosis, prognosis, and treatment by enabling precise tumor characterization and targeted therapeutic strategies. We review key genomic alterations in NSCLC, including EGFR (epidermal growth factor receptor) mutations, ALK (anaplastic lymphoma kinase) and ROS1 (ROS proto-oncogene 1) rearrangements, BRAF (B-Raf proto-oncogene serine/threonine kinase) mutations, MET (mesenchymal–epithelial transition factor) alterations, KRAS (Kirsten rat sarcoma) mutations, HER2 (human epidermal growth factor receptor 2) alterations and emerging NTRK (neurotrophic receptor tyrosine kinase) fusions and AXL-related pathways. Methods: A total of 48 patients with NSCLC was analyzed, including 22 women and 26 men (mean age 70 years, range 44–86). Tumor specimens were classified histologically as adenocarcinomas (n = 81%) or squamous cell carcinomas (n = 19%). Smoking history, PD-L1 (programmed death-ligand 1) expression, and genetic alterations were assessed. NGS (Next-generation sequencing) identified genomic variants, which were classified according to ACMG (American College of Medical Genetics and Genomics) guidelines. Results: The cohort consisted of 29 former smokers, 13 current smokers, and 5 non-smokers (12%), with a mean smoking burden of 33 pack years. PD-L1 TPS (tumor proportion score) was ≥50% in 10 patients, ≥1–<50% in 22, and <1% in 15 patients. In total, 120 genomic variants were detected (allele frequency ≥ 5%). Of these, 52 (43%) were classified as likely pathogenic or pathogenic, 48 (40%) as variants of unknown significance, and 20 (17%) as benign or likely benign. The most frequently altered genes were TP53 (tumor protein p53) (31%), KRAS and EGFR (15% each), and STK11 (serine/threonine kinase 11) (12%). Adenocarcinomas accounted for 89% of all alterations, with TP53 (21%) and KRAS (15%) being most common, while squamous cell carcinomas predominantly harbored TP53 (38%) and MET (15%) mutations. In patients with PD-L1 TPS ≥ 50%, KRAS mutations were enriched (50%), particularly KRAS G12C and G12D, with frequent co-occurrence of TP53 mutations (20%). No pathogenic EGFR mutations were detected in this subgroup. Conclusions: Comprehensive genomic profiling in NSCLC revealed a high prevalence of clinically relevant mutations, with TP53, KRAS and EGFR as the dominant drivers. The strong association of KRAS mutations with high PD-L1 expression, irrespective of smoking history, highlights the interplay between genetic and immunological pathways in NSCLC. These findings support the routine implementation of broad molecular testing to guide precision oncology approaches in both adenocarcinoma and squamous cell carcinoma patients. Full article

Background/Objectives: Head and neck squamous cell carcinoma (HNSCC) is a biologically heterogeneous malignancy with poor outcomes in advanced disease. Increasing evidence indicates that the tumor microenvironment, particularly cancer-associated fibroblasts (CAFs), plays an important role in tumor progression and immune regulation. However, the diversity of CAF subsets and their clinical relevance in HNSCC remain incompletely understood. This study aimed to characterize CAF heterogeneity and assess the prognostic significance of CAF subset-specific transcriptional programs. Methods: Single-cell RNA sequencing data from HNSCC tumors were analyzed to identify CAF subsets based on differentially expressed genes. CAF subset-specific gene signatures were used to construct prognostic risk models for overall survival (OS) and progression-free survival (PFS) in The Cancer Genome Atlas HNSCC cohort, with validation in an independent dataset. CAF-driven prognostic groups were defined, and their immune landscapes and biological pathways were evaluated. Bulk RNA sequencing of primary CAF cultures was performed for validation. Results: Six CAF subsets were identified, including myofibroblastic (myCAF), inflammatory (iCAF), antigen-presenting, and extracellular matrix-related CAFs. Risk scores derived from inflammatory CAF subsets consistently predicted shorter OS across independent cohorts, whereas PFS prediction showed greater cohort dependency. CAF-based stratification identified patient subgroups with distinct immune profiles and pathway enrichment patterns. These results were supported by validation analyses and by bulk RNA sequencing of primary CAFs, demonstrating preservation of myCAF- and iCAF-like transcriptional programs ex vivo. Conclusions: CAF heterogeneity has important prognostic and immunological implications in HNSCC. Inflammatory CAF-related transcriptional programs represent robust markers of patient survival and may complement tumor-intrinsic biomarkers. Full article

Background: Solid organ transplant recipients (SOTRs) are highly vulnerable to severe COVID-19 infection, yet initial vaccine trials provided limited data on efficacy and safety in this immunocompromised population. Heterogeneous seroconversion rates and conflicting safety reports complicate the formulation of clear clinical guidelines. This systematic review and meta-analysis aim to aggregate existing evidence to determine the precise seroconversion and safety profiles of COVID-19 vaccines and identify key factors influencing immune response in SOTRs. Methods: A comprehensive literature search was conducted identifying 125 studies evaluating WHO/FDA-authorized vaccines in SOTRs. Outcomes were the pooled seroconversion proportion and safety profile. Subgroup analyses were performed based on vaccine type, transplanted organ, number of doses, and prior SARS-CoV-2 infection status, confirmed by leave-one-out sensitivity analysis and bootstrap methods. Results: Most studies assessed mRNA-based vaccines (123/125, 98.4%). The overall pooled seroconversion proportion across all SOTRs was significantly blunted at 0.49 (95% CI, 0.43 to 0.55), demonstrating high heterogeneity (I² = 94.2%). Seroconversion showed a clear positive dose–response relationship, increasing from 27% after one dose to 84% after four doses. Prior COVID-19 infection was the strongest predictor of a response, resulting in a pooled seroconversion of 0.90 (95% CI, 0.82 to 0.94; I² = 0%). Organ-specific analyses revealed the highest response in Liver recipients (0.80) and the lowest in Lung recipients (0.29). Vaccine platform analysis showed that the highest response was with mRNA-1273 (0.55) and the lowest with CoronaVac (0.29). The safety profile was limited. Conclusions: SOTRs exhibit profound hypo responsiveness to COVID-19 vaccines; however, the extreme heterogeneity observed across studies necessitates a cautious interpretation of pooled seroconversion estimates. While the data indicates a significant dose–response relationship favoring an aggressive, multi-dose strategy, the apparent safety profile may reflect under-reporting and limited follow-up rather than confirmed safety equivalence. Rare but clinically critical outcomes, such as acute allograft rejection, remain inadequately characterized in the current literature. Consequently, while the prioritization of multi-dose regimens and hybrid immunity is supported to maximize protection, clinicians must recognize that individual responses remain highly variable, and the long-term immunological impact of repeated stimulation requires further standardized investigation. Full article