Search Results (450)

Reverse transcription quantitative real-time PCR (RT-qPCR) is a highly efficient and sensitive technique for quantifying gene transcript levels. The accuracy of gene expression analysis depends critically on the selection of appropriate reference genes for normalization, which is essential to minimize technical variation arising from differences in RNA quality, reverse transcription efficiency, and sample handling. Schisandra chinensis is a medicinally important plant with a long history of use in traditional Chinese medicine and has gained increasing global recognition. In recent years, a growing number of studies have employed molecular biology approaches to investigate the molecular mechanisms underlying secondary metabolite biosynthesis in S. chinensis. However, systematically validated reference genes for RT-qPCR analysis in this species have not yet been established. In the present study, the expression stability of eleven candidate reference genes was evaluated across different tissues and under various abiotic stress conditions in S. chinensis using four statistical algorithms: geNorm, NormFinder, BestKeeper, and RefFinder. Comprehensive analysis revealed that PP2A15 and UBC2 were the optimal reference gene combination for leaves; UBC2 and UBC11 for stems; RPL6 and PP2A15 for roots; RPL21 and RPL6 for fruits; and RPL6 and UBC11 as the best-performing pair across all tissue types. Under abiotic stress conditions, UBC11 and UBC2 exhibited the highest stability in both leaves and roots under salt stress; UBC2 and GPN1 proved most stable under alkaline stress; UBC2 and RPL6 were identified as the most suitable combination under drought stress; and UBC2 and UBQ12 demonstrated consistently stable expression across all three abiotic stress treatments. The reliability of these reference gene combinations was further validated by examining the expression profiles of three target genes. Collectively, these findings establish a validated reference gene toolkit for future gene expression studies in S. chinensis, particularly for the functional characterization of genes involved in lignan biosynthesis and abiotic stress responses. Full article

Background/Objectives: The prognostic significance of mucinous colorectal adenocarcinoma (MAC) is controversial. Some studies report good outcomes relative to conventional colorectal adenocarcinoma (CRC) as is similarly described for MACs in, e.g., the breast, lung, pancreas and prostate. However, other studies refute this, proclaiming either no difference or worse outcomes. Herein, we proffer additional insights into the biology of MAC to explain these conflicting findings. Methods: A literature search was undertaken using keywords pertaining to MAC. Archival cases from our database were analyzed to provide context for our findings. Main Findings: The unifying histologic feature of MACs is their >50% content of extracellular mucin, but they should not be viewed as a monolithic entity, as is commonly portrayed in databases. Instead, MAC is a heterogenous disease as defined by histologic and molecular phenotypes. For example, MACs arising from adenoma-like CRC have relatively good outcomes unlike those from traditional serrated adenomas. Likewise, other factors such as histologic grade (grade 1–3), genomics (e.g., BRAF, KRAS, TP53), microsatellite instability (MSI-H, MSI-L), consensus molecular subtypes (CMS1–CMS4), and mucin types (MUC2, MUC5AC) significantly influence prognosis. These pathophysiologic features, demographics (age and sex) and specific anatomic regions/topography (right/left colon/rectum) can be captured and used to improve prognostic stratification. Conclusions: In contrast to previous studies that largely demarcated MAC as a discrete entity, this paper shows the limitations of this approach by highlighting the various sub-entities comprising MAC. Recognition of this heterogeneity may help to inform future treatment algorithms. Full article

Forensic biotechnology is a rapidly evolving interdisciplinary field integrating molecular biology, genomics, and data science to address complex investigative challenges. Its applications span diverse domains, including criminalistics, food authentication, environmental monitoring, and bioterrorism preparedness. Advanced technologies such as Next-Generation Sequencing (NGS), CRISPR-Cas biosensors, and Artificial Intelligence (AI) play pivotal roles in modern diagnostics. NGS and eDNA revolutionize genetic profiling and ecological tracking, while microbiome analysis provides crucial insights into post-mortem intervals, cause of death, and geolocation. Simultaneously, CRISPR-based methods enable ultra-rapid pathogen detection, nanobiotechnology facilitates portable Lab-on-a-Chip (LOC) DNA analysis, and AI-driven algorithms optimize the interpretation of complex genomic mixtures and epigenetic age estimation. Despite these breakthroughs, significant challenges persist, including the strict legal admissibility of novel methodologies, the “black-box” dilemma in AI, ethical concerns regarding genetic privacy, and the critical need for global standardization. This review critically examines current biotechnological progress and future prospects, emphasizing the necessity of interdisciplinary collaboration to ensure reliable, accurate, and ethically sound forensic practices. Full article

Inflammatory bowel disease (IBD; Crohn’s disease and ulcerative colitis) arises from convergent dysfunction of the epithelial barrier, mucosal immunity, and gut microbiome on a background of genetic susceptibility and environmental exposures. Diet is among the most modifiable of these exposures, yet much of the diet–microbiome research in IBD remains descriptive and poorly aligned with the molecular pathways linking food to mucosal effects. This comprehensive review reframes the field around functional dysbiosis, in which altered microbial metabolic capacity (rather than taxonomic shifts alone) drives disease-relevant biology. We trace how dietary substrates and additives are converted by gut microbes into bioactive metabolites (short-chain fatty acids, secondary bile acids, tryptophan-derived indoles, sulfur compounds, and polyphenol-derived molecules) and map these to host receptors and signaling pathways governing barrier function, mucus and antimicrobial peptide production, and Treg/Th17 balance. Defined dietary therapies (exclusive enteral nutrition, the Crohn’s disease exclusion diet plus partial enteral nutrition, and Mediterranean-style patterns) are reinterpreted as interventions that reshape microbial metabolic output, and candidate biomarkers for microbiome-informed precision nutrition are evaluated. Microbiota-derived metabolites provide the molecular interface between diet and mucosal immunity in IBD; personalized dietary algorithms remain a research goal, not a validated clinical tool, and diet is best framed as adjunctive to pharmacotherapy and dietitian care. Full article

Background: Therapeutic resistance following cyclin-dependent kinase 4/6 inhibitor (CDK4/6i) plus endocrine therapy (ET) represents a key unmet need in hormone receptor-positive, human epidermal growth factor receptor 2-negative (HR+/HER2−) metastatic breast cancer (mBC). Treatment paradigms have advanced from non-targeted options, such as fulvestrant monotherapy or everolimus-based combinations, to precision medicine strategies, including inhibitors of the PI3K/AKT pathway, oral selective estrogen receptor degraders (SERDs), and novel ER-modulating agents, often guided by biomarkers and molecular surveillance. Methods: This narrative review synthesizes evidence from randomized clinical trials, real-world studies, and biomarker-driven analyses published from 2010 to 2026, with emphasis on next-generation sequencing (NGS)-guided genomic profiling, targeted pathway therapies, and circulating tumor DNA (ctDNA)-based proactive interventions in the post-CDK4/6i setting. This review was conducted and reported in accordance with the SANRA recommendations for narrative reviews. Results: Early second-line standards, including fulvestrant and alpelisib for PIK3CA-mutated tumors, established the basis for biomarker-guided treatment in hormone receptor–positive, HER2-negative metastatic breast cancer. With the widespread use of CDK4/6 inhibitors in the first-line setting, the optimal post-progression strategy has shifted toward molecularly selected combination approaches rather than single-agent endocrine therapy, as endocrine monotherapy has shown limited efficacy in acquired resistance. Multiple randomized studies have demonstrated that adding targeted agents to endocrine therapy improves progression-free survival compared with hormonal therapy alone, supporting combination regimens as the preferred strategy after CDK4/6 inhibitor progression, except in carefully selected patients with low disease burden, indolent biology, or frailty where tolerability is a major concern. Precision-based trials have further refined this approach. Elacestrant improved progression-free survival in ESR1-mutated disease in the EMERALD trial, capivasertib plus fulvestrant demonstrated significant benefit in tumors harboring AKT/PIK3CA/PTEN pathway alterations in CAPItello-291, and inavolisib plus palbociclib and fulvestrant achieved both progression-free and overall survival improvement in PIK3CA-mutated patients with early relapse in INAVO120. Real-world analyses further support the effectiveness of these biomarker-directed strategies across diverse clinical subgroups. Comprehensive genomic profiling has identified multiple resistance mechanisms, including ESR1 mutations, PI3K/AKT/mTOR pathway activation, RB1 loss, and FGFR alterations, which may co-occur and reduce sensitivity to endocrine monotherapy. While ESR1 and PI3K pathway alterations now guide approved therapies, FGFR alterations remain investigational targets, with ongoing trials evaluating selective FGFR inhibitors. Proactive switching approaches evaluated in SERENA-6 and PADA-1 demonstrate that serial circulating tumor DNA (ctDNA) monitoring can detect emergent ESR1 mutations before radiographic progression, providing a clinically actionable lead time for early therapeutic modification and extending endocrine-based disease control by approximately 5 to 7 months. Conclusions: Post-CDK4/6i management increasingly relies on NGS-guided precision approaches, integrating pathway-specific therapies and ctDNA surveillance to tailor sequencing based on resistance profiles, prior ET response, and tumor heterogeneity. Future investigations into novel ER degraders and multi-targeted combinations hold potential to further optimize algorithms, extend non-chemotherapy options, and enhance survival in HR+/HER2− mBC. Full article

Cardiac arrhythmias are increasingly recognized as dynamic clinical phenotypes arising from the interplay between myocardial substrate, systemic biology, and modifiable exposures rather than as isolated disorders of cardiac electrophysiology. Advances in the field have broadened the conceptual framework of arrhythmia medicine to include structural remodeling, inflammation, autonomic dysfunction, metabolic perturbation, endothelial injury, and aging-related vulnerability as central determinants of arrhythmic risk, progression, and treatment response. In parallel, diagnostic paradigms are evolving from rhythm classification alone toward multidimensional phenotyping that integrates clinical, physiological, and imaging-based markers to identify susceptibility earlier and with greater precision. These developments are also reshaping therapeutic strategy, supporting a shift from uniform treatment algorithms toward individualized care in which rhythm control, surveillance, risk-factor modification, anticoagulation, and antiarrhythmic drug selection are aligned with the underlying biological context. This more integrated view positions arrhythmias not simply as electrical events to be suppressed, but as manifestations of broader cardiovascular and systemic disease processes that require mechanistically informed and phenotype-directed management. Full article

Cancer research has undergone a fundamental transformation in recent decades due to the integration of artificial intelligence (AI) models into the study of tumor biology. However, tumor evolution, driven by genetic and phenotypic alterations leading to heterogeneity, resistance and metastasis, remains a major challenge in oncology. To understand these processes is crucial for developing effective therapeutic strategies and improving patient outcomes. Conventional methods often fail to capture the complexity and dynamics of these processes. In contrast, AI tools have the ability to integrate and analyze large-scale multi-omics, imaging and clinical data, offering the capability to decode tumor complexity. AI-driven methods facilitate multi-modal data integration, enabling the recognition of patterns that connect molecular alterations with phenotypic outcomes. In functional genomics, AI tools predict the effects of genetic variants, identify regulatory elements and map dysregulated pathways, thus clarifying mechanisms underlying tumor development and resistance. In the imaging field, deep learning techniques improve tumor segmentation, characterization and longitudinal monitoring, providing more accurate insights into tumor progression and treatment response. Predictive modeling could allow the anticipation of tumor evolution and drug response, supporting adaptive therapeutic plans and real-time treatment adjustments. Moreover, AI supports biomarker discovery, patient stratification and decision support systems that can improve clinical trial design and accelerate the development of personalized therapies. However, these advances raise important ethical challenges, including data privacy, algorithmic bias and the preservation of patient autonomy. Addressing these concerns is essential to ensure the responsible deployment of AI in oncology. Full article

Pediatric neurodevelopmental disorders (NDDs) encompass a highly heterogeneous group of conditions characterized by complex interactions among genetic, molecular, developmental, and environmental factors. Growing evidence increasingly supports an important role for neuroglial dysfunction, including disturbances in astrocytic, microglial, and oligodendroglial biology, in the pathophysiology of disorders such as autism spectrum disorder, global developmental delay, intellectual disability, and rare neurogenetic syndromes. At the same time, artificial intelligence (AI)-assisted analytical approaches are becoming increasingly relevant in pediatric diagnostics through integration of multidimensional datasets, including clinical phenotypes, neuroimaging, genomic sequencing, and molecular biomarkers. This review examines the evolving intersection of neuroglial biology and AI-based analytical methods in pediatric NDDs. Current understanding of neuroglial mechanisms underlying disease vulnerability and developmental heterogeneity is discussed alongside emerging applications of machine learning, deep phenotyping platforms, radiogenomics, and large language models in diagnostic interpretation and clinical decision support. Important translational and ethical challenges, including algorithmic bias, interpretability limitations, data governance, and disparities in data accessibility, are also considered. Overall, integration of neuroglial research with AI-assisted analytical frameworks may contribute to more biologically informed interpretation of pediatric neurodevelopmental disorders and support ongoing development of increasingly individualized diagnostic approaches. Full article

Juvenile myelomonocytic leukemia (JMML) is a rare, aggressive myeloproliferative neoplasm of early childhood characterized by constitutive activation of the RAS-MAPK signaling pathway. RASopathies are a heterogeneous group of complex genetic disorders arising from germline mutations that dysregulate RAS-MAPK signaling. Noonan syndrome, CBL syndrome, and neurofibromatosis type 1 (NF1) are the three major RASopathies predisposing to JMML. More than 90% of JMML cases harbor germline or somatic mutations in one of five canonical driver genes—PTPN11, NRAS, KRAS, NF1, or CBL—establishing JMML as the prototypical malignant manifestation of RASopathy biology. The fifth edition of the World Health Organization Classification of Tumours reclassified JMML as a myeloproliferative neoplasm while the International Consensus Classification adopted JMML under pediatric and/or germline mutation-associated disorders, introducing a JMML-like category for cases lacking five canonical mutations but harboring emerging drivers such as SH2B3::LNK alterations and ALK::ROS1 fusions. The distinction between germline and somatic mutations profoundly influences prognosis: e.g., germline PTPN11-associated myeloproliferations and many germline CBL cases undergo spontaneous resolution, whereas somatic PTPN11- and NF1-mutated JMML is more aggressive and requires prompt allogeneic hematopoietic stem cell transplantation. DNA methylation profiling has emerged as the most robust prognostic framework, with consensus defining high-, intermediate-, and low-methylation subgroups that independently predict outcome. Both genotype and DNA methylation subclassification have been integrated into clinical decision-making, incorporating pretransplant azacitidine, watch-and-wait approaches for favorable-risk patients, and emerging targeted therapies including MEK inhibitors. This review synthesizes recent advances in understanding JMML as a bona fide RASopathy; provides a diagnostic algorithm, molecular landscapes, and prognostic models; and highlights opportunities for molecularly targeted therapeutic intervention. Full article

Desmoid tumors (DTs) are rare, fibroblastic neoplasms characterized by locally aggressive behavior, unpredictable clinical trajectories, and a substantial impact on patient quality of life despite minimal metastatic potential. Although the underlying biology of DTs remains incompletely defined, associations with prior trauma, hormonal exposure, and aberrant Wnt/β-catenin signaling—including somatic CTNNB1 mutations and germline APC alterations seen in Familial Adenomatous Polyposis—have informed both historical and contemporary therapeutic approaches. Management strategies have evolved from surgery-dominant paradigms toward individualized, multimodal treatment algorithms emphasizing systemic medical therapy, as reflected in current NCCN and Desmoid Tumor Working Group recommendations. This review focuses on the medical management of DTs, tracing the evolution from earlier noncytotoxic therapies, including antiestrogen agents such as tamoxifen, to modern systemic options supported by prospective and randomized data. We summarize available evidence for four principal classes of medical therapy: nonsteroidal anti-inflammatory drugs, cytotoxic chemotherapy (with particular emphasis on anthracycline-based regimens), tyrosine kinase inhibitors—most notably sorafenib—and the emerging class of γ-secretase inhibitors. Recent phase III data supporting the efficacy of nirogacestat highlight a shift toward mechanism-based, targeted treatment with demonstrable benefits in progression-free survival, symptom control, and patient-reported outcomes. Collectively, these advances underscore a maturing therapeutic landscape in which systemic therapy plays a central role in disease control, symptom palliation, and preservation of function for patients with advanced desmoid tumors. Full article

This paper proposes a high-precision theoretical and computational neurorehabilitation framework for Mild Cognitive Impairment (MCI), connecting computational neuroscience and clinical practice through qEEG-guided neurofeedback training (NFT). By employing sLORETA to identify putative pathological nodes within the Default Mode Network (DMN)—specifically the Precuneus and the Posterior Cingulate—the model utilizes spectral decomposition to isolate the aperiodic 1/f component, reducing background noise bias and allowing the calculation of a pure individual alpha frequency (IAF) to inform recalibration of Weber’s Cognitive Threshold. The core architecture uses Bayesian algorithms and stochastic modeling to drive a Dynamic Weight Change mechanism. To support Long-Term Potentiation (LTP) and Hebbian learning, reward thresholds are modulated in real time to target a 70% success rate, as a strategic rationale to anticipate neural fatigue while maintaining the Reward Prediction Error required for synaptic strengthening. As a prospective validation pathway, future studies may assess clinical value through changes in MoCA and RAVLT scores, as well as by examining normalization of cortical coherence in the Default Mode Network (DMN). By merging computational neuroscience with biological models of synaptic plasticity, this work outlines how individual biology can be mapped into an explicit mathematical model. The proposed framework may inform an individualized protocol that provides an objective model-based measure of cognitive recovery, suggesting a replicable and robust strategy for neurorehabilitation during the prodromal phase of dementia, and providing a new approach to neuroscience-based cognitive rehabilitation. This work is intended as a theoretical and computational framework; no complete empirical dataset is reported in the present manuscript. Full article

Extramedullary relapse (EMR) of acute lymphoblastic leukemia (ALL) after allogeneic hematopoietic stem cell transplantation (Allo-HSCT) represents a clinically and biologically distinct entity compared with medullary relapse, characterized by marked heterogeneity, compartmental immune escape mechanisms, and generally poor prognosis. EMR arises at the intersection of clonal resistance, evolutionary disease adaptation, and heterogeneous distribution of the graft-versus-leukemia effect, resulting in evolutionary trajectories that are often dissociated between medullary and extramedullary compartments. In the absence of prospectively validated therapeutic algorithms, EMR management requires a structured and adaptive approach based on multidimensional assessment integrating leukemia biology, disease burden and anatomical distribution, bone marrow minimal residual disease (MRD) status, and immune reconstitution. Therapeutic strategies include local treatments, targeted agents, immunotherapies, and immunomodulatory interventions, applied within a dynamic sequence tailored to treatment response. Follow-up plays a central role as an active tool for prognostic stratification and clinical decision-making, enabling early detection of systemic progression and optimization of the timing of consolidative strategies, including second Allo-HSCT in selected patients. An integrated and biologically driven management of post-Allo-HSCT EMR is essential to improve outcomes in this high-risk clinical setting. Full article

Artificial intelligence (AI) is increasingly used in cancer research, enabling integrative analysis of complex biomedical data to identify actionable therapeutic vulnerabilities. This review specifically examines how AI advances mechanistic cancer target discovery and translational drug development, focusing on: (1) the processing of large-scale genomics, transcriptomics, proteomics, metabolomics, single-cell profiling, spatial, and clinical datasets using machine learning (ML) and deep learning (DL) algorithms; (2) the identification of candidate biomarkers, driver genes, dysregulated pathways, tumor dependencies, and molecular targets that traditional methods often miss; (3) the integration of multi-omics data, network biology, causal inference, and systems-level modeling to refine mechanistic understanding of cancer progression and separate functional driver events from passengers; and (4) applications in drug development, including virtual screening, molecular modeling, structure-informed target validation, drug repurposing, synthetic lethality prediction, and de novo drug design, which collectively may enhance early-stage drug discovery efficiency. The review underscores that AI serves as both a predictive tool and a platform for linking molecular mechanisms to hypothesis generation, target prioritization, and rational treatment design. Challenges such as data heterogeneity, algorithmic bias, interpretability, reproducibility, regulatory requirements, and patient privacy must be addressed for robust translation and clinical use. Future directions may focus on hybrid approaches that integrate causal modeling, explainable AI, multimodal data, and experimental validation to yield mechanistically grounded, clinically actionable insights. AI-driven approaches ultimately aim to accelerate mechanism-based cancer target discovery and enable more precise, biologically informed anticancer therapies. Full article

Science universities strive to offer efficient lab training to their students and at the same time secure their safety and minimize the damages to the lab equipment. Thus, the development of distance learning tools for students to be trained virtually and safely in using the various lab instruments and performing experiments is necessary. Since the students are evaluated for their performance at the on-site labs, the assessment at the virtual labs is also needed and consequently, an embedded assessment mechanism for the evaluation of the user’s performance in the virtual lab is a necessary feature. For the assessment mechanism to be reliable and devoid of the designer’s bias, though, it may need calibration with Machine Learning. Hellenic Open University has developed its own virtual biology laboratory, Onlabs, which simulates its on-site one for its students to be trained and evaluated at. Considering the evaluation of the user’s performance in Onlabs, it is made with respect to particular experiments and is based on an embedded scoring algorithm. The latter is two-fold, measuring the extent to which the necessary steps have been made and the extent to which those steps were made in the correct order. Within the context of the experimental procedure of microscoping, the scoring algorithm has been recalibrated with the use of various Machine Learning techniques. In this paper, we propose the design of a Reinforcement Learning variant and recalibration of the scoring measure concerning the steps order. The results suggest that under specific parameters and Reinforcement Learning methods, a more efficient scoring mechanism may be achieved. Full article

Treatment-resistant schizophrenia (TRS) remains a significant clinical challenge due to limited therapeutic options and a poor understanding of its underlying biology. Recent findings suggest that immune system dysregulation may play a critical role in the pathophysiology of TRS. This systematic review aimed to synthesize current evidence on immunological abnormalities associated with TRS, with a focus on inflammatory markers, immune cell profiles, and the role of autoantibodies, and to explore their potential utility in diagnosis and treatment. A systematic review of the literature was conducted in accordance with PRISMA guidelines, incorporating clinical, molecular, and translational studies on immunological markers in patients with TRS. Included studies assessed cytokine levels, immune cell phenotypes, autoantibodies, genetic factors, and the effects of immunomodulatory therapies. Emphasis was placed on findings differentiating TRS from treatment-responsive schizophrenia. TRS is associated with distinct immune profiles, including elevated IL-6, IL-8, TNF-α, and sCD25 levels, overexpression of CD33 on monocytes and expansion of CD123+ plasmacytoid dendritic cells. Autoantibodies, particularly those targeting glutamatergic receptors, are more prevalent in TRS and decrease with clozapine treatment. Predictive models using IgM autoantibodies and genetic variants show promise for early identification of at-risk individuals. Emerging immunomodulatory treatments such as rituximab, levamisole, and senolytics are under investigation, offering potential for personalized strategies. Immunological dysfunction represents a reproducible and biologically relevant feature of TRS. Integration of immune biomarkers into clinical practice may enhance diagnostic precision and inform personalized therapeutic approaches. Future research should prioritize standardized biomarker protocols and longitudinal studies to validate causal associations and optimize treatment algorithms. Full article