Search Results (407)

Despite technological progress, glioblastoma (GBM) continues to confer dismal prognoses. Modern neuroimaging methods are assuming an ever greater role in diagnosing and monitoring brain tumors. This review shows current neuroimaging approaches and systemic therapeutic strategies for glioblastoma, with a focus on emerging and innovative treatments. Advances in multiparametric magnetic resonance imaging—MRI (diffusion, perfusion, and spectroscopy) and novel positron emission tomography (PET) tracers, complemented by radiomics and artificial intelligence (AI), now refine tumor delineation, differentiate progression from treatment effects, and may help predict treatment responses. Maximal safe resection followed by chemoradiotherapy with temozolomide remains the standard, with the greatest benefit seen in O⁶-methylguanine DNA methyltransferase (MGMT) promoter-methylated tumors. Bevacizumab and other targeted modalities offer mainly progression-free, not overall survival, gains. Immune checkpoint inhibitors (e.g., nivolumab) have not improved survival in unselected GBM, while early multi-antigen CAR-T (chimeric antigen receptor T-cell) strategies show preliminary bioactivity without established durability. While actionable alterations (NTRK fusions and BRAF V600E) justify selective targeted therapy trials, their definitive benefit in classical GBM is unproven. Future priorities include harmonized imaging molecular integration, AI-driven prognostic modeling, novel PET tracers, and strategies to breach or transiently open the blood–brain barrier to enhance drug delivery. Convergence of these domains may convert diagnostic precision into improved patient outcomes. Full article

Obesity is a multifaceted disorder influenced by various factors, with heredity being a significant contributor. Bariatric surgery is the most effective long-term intervention for morbid obesity and associated comorbidities, while outcomes vary significantly across individuals. Recent studies indicate that genetic and molecular determinants, particularly alterations in the leptin–melanocortin signalling pathway involving the fat mass and obesity-associated gene (FTO), pro-opiomelanocortin (POMC), melanocortin 4 receptor (MC4R), leptin (LEP), and leptin receptor (LEPR), influence the efficacy of weight loss and metabolic adaptations post-surgery. This narrative review consolidates evidence from peer-reviewed papers available in PubMed and Scopus until July 2025. The emphasis was on novel research and systematic reviews examining genetic polymorphisms, gene–environment interactions, and outcomes following bariatric procedures such as Roux-en-Y gastric bypass (RYGB) and sleeve gastrectomy (SG). Recent research emphasised the integration of genetic screening and precision medicine models into clinical bariatric workflows. Variants in FTO (e.g., rs9939609), MC4R (e.g., rs17782313), LEPR, and POMC are associated with diminished weight loss post-surgery, an increased likelihood of weight regain, and reduced metabolic enhancement. Patients with bi-allelic mutations in MC4R, POMC, or LEPR exhibited poor long-term outcomes despite receiving effective physical interventions. Furthermore, genes regulating mitochondrial metabolism (such as PGC1A), adipokine signalling (such as ADIPOQ), and glucose regulation (such as GLP1R) have been demonstrated to influence the body’s response to sugar and the extent of weight gain or loss. Two recent systematic reviews elucidate that candidate gene investigations are beneficial; however, larger genome-wide association studies (GWAS) and machine learning techniques are necessary to enhance predictive accuracy. Integrating genetic and molecular screening with bariatric surgery planning possesses significant therapeutic potential. Genotyping can assist in patient selection, procedural decisions, and medication additions, particularly for those with variants that influence appetite regulation or metabolic flexibility. Advancements in precision medicine, including the integration of polygenic risk scores, omics-based profiling, and artificial intelligence, will enhance the customisation of surgical interventions and extend the lifespan of individuals with severe obesity. The epigenetic regulators of energy balance DNA methylation, histone changes, and microRNAs that may affect individual differences in weight-loss patterns after bariatric surgery are also briefly contextualised. We discuss the concept that epigenetic modulation of gene expression, mediated by microRNAs in response to food and exercise, may account for variations in metabolic outcomes post-surgery. Full article

Background: Bone marrow-derived mesenchymal stem cell exosomes (EXOs) are attractive in biotechnology and biomedical research, as they possess natural cell-targeting properties and can cross biological barriers by influencing the SDF-1/CXCR4 axis. Lipid calcium phosphate (LCP) consists of a calcium phosphate core and an asymmetric phospholipid bilayer containing abundant Ca²⁺ ions. AMD3100 modification of targeted LCP (T-LCP) can achieve targeted delivery to ischemic lesions via specific binding to CXCR4 receptors on various neuronal cell surfaces. Methods: Herein, a fused membrane formulation that simultaneously possesses EXO characteristics and enables targeted modification with AMD3100 was produced. The characteristics of biologically derived EXOs, artificially designed T-LCP, and the fused membrane formulation, including targeted delivery and gene loading efficiency, were then compared. Results: The fusion of artificially designed T-LCP with EXOs of natural origin is feasible and combines the advantages of both to achieve more prominent targeted delivery effects. Conclusions: MiRNA210-based gene therapy was effective in this study and provides a strategy for therapeutic efficacy in delivery systems with different targeting efficiencies. Full article

Background: Biopsy remains the gold standard for characterizing breast cancer, but it is invasive, costly, and may not fully capture tumor heterogeneity. Advances in artificial intelligence (AI) now allow for the extraction of biological and clinical information from medical images, raising the possibility of using imaging as a non-invasive alternative. Methods: A semi-systematic review was conducted to identify AI-based approaches applied to mammography (MM) and breast tomosynthesis (BT) for tumor subtyping, staging, and prognosis. A PubMed search retrieved 1091 articles, of which 81 studies met inclusion criteria (63 MM, 18 BT). Studies were analyzed by clinical target, modality, AI pipeline, number of cases, dataset type, and performance metrics (AUC, accuracy, or C-index). Results: Most studies focused on tumor subtyping, particularly receptor status and molecular classification. Contrast-enhanced spectral mammography (CESM) was frequently used in radiomics pipelines, while end-to-end deep learning (DL) approaches were increasingly applied to MM. Deep models achieved strong performance for ER/PR and HER2 status prediction, especially in large datasets. Fewer studies addressed staging or prognosis, but promising results were obtained for axillary lymph node (ALN) metastasis and pathological complete response (pCR). Multimodal and longitudinal approaches—especially those combining MM or BT with MRI or ultrasound—show improved accuracy but remain rare. Public datasets were used in only a minority of studies, limiting reproducibility. Conclusions: AI models can predict key tumor characteristics directly from MM and BT, showing promise as non-invasive tools to complement or even replace biopsy. However, challenges remain in terms of generalizability, external validation, and clinical integration. Future work should prioritize standardized annotations, larger multicentric datasets, and integration of histological or transcriptomic validation to ensure robustness and real-world applicability. Full article

Heart failure represents a complex clinical syndrome characterized by progressive ventricular dysfunction, systemic congestion, and high mortality despite significant advances in pharmacological and device-based therapy. This review explores recent developments across the heart failure continuum, with a focus on therapeutic advances across the continuum of care, with emphasis on both established and emerging strategies. In patients with reduced ejection fraction, early initiation of the four pillars markedly lowers cardiovascular events, yet real-world implementation remains limited by therapeutic inertia and underdosing. Novel agents such as finerenone provide cardiorenal benefits in patients with diabetes and chronic kidney disease, while glucagon-like peptide-1 receptor agonists show promise in preserved or mildly reduced ejection fraction, particularly with obesity. Tricuspid regurgitation, once considered a secondary phenomenon, is now recognized as a modifiable contributor to disease progression, with transcatheter interventions offering new therapeutic avenues. In advanced disease, innovations including donation after circulatory death and the development of total artificial heart systems offer promising solutions to overcome organ shortages and improve access to transplantation. Together, these advances highlight a shift toward precision-guided, multidisciplinary heart failure care. Full article

Regulatory T cells (Tregs) are a specialized subset of CD4+ T lymphocytes essential for maintaining immune tolerance and preventing autoimmunity. However, in breast cancer, tumors exploit Tregs to establish an immunosuppressive microenvironment that enables immune evasion, accelerates progression, and contributes to therapeutic resistance. This review synthesizes current evidence on the role of Tregs in invasive breast cancer (IBC), highlighting their prognostic significance across molecular subtypes, mechanisms of immune suppression, and impact on treatment response. We integrated mechanistic and clinical insights to discuss opportunities for Treg-targeted therapeutic strategies, with attention paid to challenges such as autoimmunity, compensatory resistance, and subtype-specific heterogeneity. Finally, we outline future directions, including biomarker-driven precision medicine, novel therapeutic combinations, advanced preclinical models, as well as potential artificial intelligence-assisted approaches that aim to selectively disrupt tumor-promoting Treg functions while preserving the systemic immune balance. Full article

As nutrient allocation trade-offs occur between reproductive and vegetative development in crops, optimizing their partitioning holds promise for improving agricultural productivity and quality. Herein, we characterize the phenotypic diversity of the fruitfulness trait and identify associated genes in tea plants (Camellia sinensis). Over three consecutive years, we monitored the fruitfulness of an F₁ hybrid population (n = 206) derived from crosses of ‘Emei Wenchun’ and ‘Chuanmu 217’. A marked variation was observed in the yield of individual plants, ranging from complete sterility (zero fruits) to exceptionally high fertility (1612 fruits). Using the high-resolution genetic linkage map and the fruitfulness data, we identified a stable major QTL designated as qFN5. To fine-map the underlying gene(s), artificial pollination experiments were conducted with extreme phenotype individuals (with the highest vs. lowest fruit numbers). Bulked segregant RNA sequencing (BSR-Seq) with ovules collected at two and seven days post-pollination (DPP) identified the genomic intervals that exhibit a high degree of overlap with qFN5. Analysis of expression dynamics combined with functional genomics data revealed a prominent candidate gene, CsETR2 (TGY048509), which encodes an ethylene receptor protein. When CsETR2 was overexpressed in Arabidopsis thaliana, the transgenic lines exhibited significantly decreased reproductive performance relative to the wild-type plants. Relative to the wild type, the transgenic lines exhibited a significant decline in several key traits: the number of effective panicles decreased by 72.5%, the seed setting rate dropped by 67.7%, and the silique length shortened by 38%. These findings demonstrate its role in regulating plant fruitfulness. Furthermore, yeast one-hybrid and dual-luciferase assays verified that CsMYB15 (TGY110225) directly binds to the CsETR2 promoter, thus repressing its transcription. In summary, our findings expand the understanding of genetic regulation underlying fruitfulness in tea plants and provide candidate target loci for breeding. Full article

Obesity is a multifactorial chronic disease resulting from complex genetic, molecular, environmental, and behavioral interactions. Its prevalence rises worldwide, affecting cardiovascular, metabolic, oncological, hepatic, respiratory, and skeletal health. Beyond caloric excess, genetic predisposition, epigenetic modifications, gut microbiota dysbiosis, endocrine-disrupting agents, circadian misalignment, and intergenerational and prenatal influences are critical determinants of obesity risk. Core pathophysiological mechanisms include insulin resistance, dyslipidemia, chronic low-grade inflammation, and neuroendocrine dysregulation of appetite and energy balance. These processes are linked to comorbidities such as type 2 diabetes, hypertension, atherosclerosis, fatty liver disease, sleep apnea, osteoporosis, and cancer. Advances in molecular profiling, metabolic phenotyping, and body composition analysis are refining obesity classification and enabling precise risk stratification. Current therapeutic strategies include behavioral interventions addressing stress-related mechanisms, pharmacological therapies such as GLP-1 receptor agonists, emerging gene therapy approaches, and bariatric surgery. Gut-derived hormones (leptin, ghrelin, GLP-1, PYY, CCK) are recognized as pivotal regulators of appetite and weight. Preventive strategies increasingly emphasize circadian alignment, while epigenetic inheritance and prenatal exposures such as maternal obesity or smoking highlight early-life programming in future metabolic health. Additionally, artificial intelligence-based platforms and personalized nutrition provide innovative opportunities for individualized prevention and management. This review synthesizes contemporary evidence on the biological basis, systemic consequences, preventive strategies, and evolving therapeutic modalities of obesity, affirming its recognition as a complex chronic disease requiring personalized, multidisciplinary care. Full article

Background/Objectives: In many low- and middle-income countries (LMICs), including Bosnia and Herzegovina, oncology services are constrained by a limited number of specialists and uneven access to evidence-based care. Artificial intelligence (AI), particularly large language models (LLMs) such as ChatGPT, may provide clinical decision support to help standardize treatment and assist clinicians where oncology expertise is scarce. This study aimed to evaluate the concordance, safety, and clinical appropriateness of ChatGPT-generated treatment recommendations compared to decisions made by a multidisciplinary team (MDT) in the management of newly diagnosed breast cancer patients. Methods: This retrospective study included 91 patients with newly diagnosed, treatment-naïve breast cancer, presented to an MDT in Bosnia and Herzegovina in 2023. Patient data were entered into ChatGPT-4.0 to generate treatment recommendations. Four board-certified oncologists, two internal and two external, evaluated ChatGPT’s suggestions against MDT decisions using a 4-point Likert scale. Agreement was analyzed using descriptive statistics, Cronbach’s alpha, and Fleiss’ kappa. Results: The mean agreement score between ChatGPT and MDT decisions was 3.31 (SD = 0.10), with high consistency across oncologist ratings (Cronbach’s alpha = 0.86). Fleiss’ kappa indicated moderate inter-rater reliability (κ = 0.31, p < 0.001). Higher agreement was observed in patients with hormone receptor-negative tumors and those treated with standard chemotherapy regimens. Lower agreement occurred in cases requiring individualized decisions, such as low-grade tumors or uncertain indications for surgery or endocrine therapy. Conclusions: ChatGPT showed high concordance with MDT treatment plans, especially in standardized clinical scenarios. In resource-limited settings, AI tools may support oncology decision-making and help bridge gaps in clinical expertise. However, careful validation and expert oversight remain essential for safe and effective use in practice. Full article

This manuscript provides an in-depth review of both artificial and natural sweeteners, including polyols and plant-derived alternatives, examining their sweetening potency, glycemic index, modes of action, and applications in the food sector. The rising demand for sugar substitutes, fueled by health concerns such as obesity and diabetes, has prompted significant research into low-calorie and non-nutritive sweeteners. This work categorizes sweeteners into synthetic options (such as aspartame, sucralose, saccharin) and naturally occurring ones (such as stevia, monk fruit, and polyols like sorbitol, xylitol, erythritol), focusing on physico-chemical characteristics, relative sweetness (ranging from 100 to 220,0000 times sweeter than sucrose), and glycemic index, important for their use in diabetes-friendly food products. The current manuscript examines how these sweeteners interact with taste receptors to induce sweetness perception without contributing significant calories. It also discusses their health implications and controversies and limitations regarding healthy and safety data, process feasibility, market application trends, environmental stability, and commercialization challenges. The review also addresses challenges in scaling production and ensuring the economic viability of plant-based sweeteners, offering a forward-looking perspective on their commercialization in the food industry. Full article

Cardiovascular-kidney-metabolic (CKM) syndrome is a multifaceted, systemic disorder characterized by the interplay of cardiovascular disease (CVD), chronic kidney disease (CKD), type 2 diabetes mellitus (T2DM), and obesity. This review synthesizes current and emerging therapeutic strategies aimed at addressing the shared pathophysiologic mechanisms driving CKM progression, such as insulin resistance, inflammation, oxidative stress, and neurohormonal activation. Established pharmacotherapies that include sodium-glucose cotransporter 2 (SGLT2) inhibitors, glucagon-like peptide-1 receptor agonists (GLP-1 RAs), and nonsteroidal mineralocorticoid receptor antagonists like finerenone have demonstrated robust efficacy in reducing cardiovascular events, slowing renal decline, and improving metabolic outcomes. Additionally, novel agents targeting lipoprotein(a), interleukin-6, and hepatic fat accumulation are expanding the therapeutic landscape. RNA-based therapies, including antisense oligonucleotides (ASOs) and small interfering RNAs (siRNAs), are designed to modulate lipoprotein(a) and PCSK9 expression. Artificial intelligence (AI) is also emerging as a transformative tool for personalized CKM management, enhancing risk prediction and clinical decision-making. The review highlights the relevance of metabolic dysfunction-associated steatotic liver disease (MASLD) as a CKM modifier and discusses the approval of resmetirom, a selective thyroid hormone receptor β agonist, for noncirrhotic MASH. By integrating evidence from clinical trials, mechanistic studies, and emerging technologies, this review provides a comprehensive resource for clinicians and researchers navigating the evolving field of CKM syndrome. Full article

Chimeric antigen receptor (CAR) T cell therapy has revolutionized the treatment of certain hematologic malignancies, yet its success in solid tumors has been limited by antigen heterogeneity, an immunosuppressive tumor microenvironment, and barriers to cell trafficking and persistence. To expand the reach of cellular immunotherapy, multiple immune cell types—γδ T cells, invariant NKT cells, virus-specific T cells, natural killer (ΝΚ) cells, and myeloid effectors such as macrophages and dendritic cells—are now being explored as alternative or complementary CAR platforms. Each lineage brings unique advantages, such as the innate cytotoxicity and safety profile of CAR NK cells, the tissue infiltration and microenvironment-modulating capacity of CAR macrophages, or the MHC-independent recognition offered by γδ T cells. Recent advances in pharmacological strategies, synthetic biology, and artificial intelligence provide additional opportunities to overcome barriers and optimize CAR design and manufacturing scale-up. Here, we review the state of the art in engineering diverse immune cells for solid tumor therapy, highlight safety considerations across autologous, allogeneic, and in vivo CAR cell therapy approaches, and provide our perspective on which platforms might best address current unmet clinical needs. Collectively, these developments lay the foundation for next-generation strategies to achieve durable immunotherapy responses in solid tumors. Full article

Background: Chimeric Antigen Receptor (CAR) T-cell therapy has transformed the treatment of hematological malignancies, yet its application in solid tumors remains constrained by unique biological and logistical barriers. Objective: This review critically examines the evolving landscape of CAR T-cell therapy in solid malignancies, with a focus on antigen heterogeneity, the immunosuppressive tumor microenvironment, and risks of on-target, off-tumor toxicity. Methods: We outline recent advances in CAR engineering, including co-stimulatory optimization, dual- and multi-antigen targeting, armored CARs, and gene-edited constructs designed to enhance persistence and anti-tumor activity. Clinical progress is highlighted by recent FDA approvals of genetically modified T-cell therapies in synovial sarcoma and melanoma, underscoring the potential for broader solid tumor application. Additionally, we synthesize early-phase clinical trial findings across multiple solid tumor types (e.g., lung, colorectal, ovarian, glioblastoma), and discuss innovative approaches such as regional delivery, checkpoint blockade combinations, and incorporation of chemokine receptors for improved tumor infiltration. The review also considers future strategies, including artificial intelligence-guided target discovery and rational trial design to overcome translational bottlenecks. Conclusions: With expanding clinical experience and continued technological innovation, CAR T-cell therapy is steadily transitioning from an experimental strategy to a therapeutic reality in solid tumors, poised to reshape the future of cancer immunotherapy. Full article

Lung cancer is the leading cause of cancer mortality globally, despite the advancements in screening and management. Survival rates for lung cancer remain suboptimal, largely due to late-stage diagnoses and tumor heterogeneity. Recent advancements in artificial intelligence and radiomics provide a promising outlook for lung cancer screening, diagnosis, personalized treatment, and prognosis. These advances use large-scale clinical and imaging datasets that help identify patterns and predictive features that may be missed by human interpretation. Artificial intelligence tools hold the potential to take clinical decision-making to another level, thus improving patient outcomes. This review summarizes current evidence on the applications, challenges, and future directions of artificial intelligence (AI) in lung cancer care, with an emphasis on early diagnosis and personalized treatment. We examine recent developments in AI-driven approaches, including machine learning and deep neural networks, applied to imaging (radiomics), histopathology, biomarker analysis, and multi-omic data integration. AI-based models demonstrate promising performance in early detection, risk stratification, molecular profiling (e.g., programmed death-ligand 1 (PD-L1) and epidermal growth factor receptor (EGFR) status), and outcome prediction. These tools may enhance diagnostic accuracy, optimize therapeutic decisions, and ultimately improve patient outcomes. However, significant challenges remain, including model heterogeneity, limited external validation, generalizability issues, and ethical concerns related to transparency and clinical accountability. AI holds transformative potential for lung cancer care but requires further validation, standardization, and integration into clinical workflows. Multicenter collaborations, regulatory frameworks, and explainable AI models will be essential for successful clinical adoption. Full article

Background/Objectives: Sex determination and differentiation exhibit remarkable molecular diversity across taxa, driven by genetic, epigenetic, and environmental factors. Invertebrates with sequential hermaphroditism, such as the Pacific oyster (Magallana gigas), represent a poorly understood system despite their role as keystone species and contribution to a substantial aquaculture industry. Methods: To identify sex-related molecular markers during gametogenesis, we repeatedly sampled hemolymph from artificially conditioned oysters over two months, and sex phenotypes were assigned at the end of the experiment by biopsy. Results: RNA-sequencing analysis of five males and five females revealed subtle yet consistent sex-specific transcriptional signatures in hemolymph. We show that gametogenesis proceeds asynchronously among oysters, even within the same sex individuals. Complex physiological trade-offs were discovered between sexes during gonad maturation; in early stages of sexual maturation, females prioritized cell division, whereas males suppressed it. Females exhibited higher expression of solute carrier family (SLC) genes, suggesting enhanced nutrient exchange during oogenesis. Temporal dynamics highlighted differential expression of genes regulating cross-membrane ion gradients (e.g., transient receptor potential channels) and signal transduction (e.g., signal transducer and activator of transcription), previously linked to environmental sex determination (ESD) in some reptilian species. Conclusions: Together, these findings underscore that gametogenesis in Pacific oysters is complex and dynamic, and that molecular pathways of ESD may be partially conserved between invertebrate and vertebrate species. Full article