Search Results (196)

Background: Artificial intelligence (AI) is increasingly applied in the diagnosis of pediatric rare diseases, enhancing the speed, accuracy, and accessibility of genetic interpretation. These advances support the ongoing shift toward personalized medicine in clinical genetics. Objective: This review examines current applications of AI in pediatric rare disease diagnostics, with a particular focus on real-world data integration and implications for individualized care. Methods: A narrative review was conducted covering AI tools for variant prioritization, phenotype–genotype correlations, large language models (LLMs), and ethical considerations. The literature was identified through PubMed, Scopus, and Web of Science up to July 2025, with priority given to studies published in the last seven years. Results: AI platforms provide support for genomic interpretation, particularly within structured diagnostic workflows. Tools integrating Human Phenotype Ontology (HPO)-based inputs and LLMs facilitate phenotype matching and enable reverse phenotyping. The use of real-world data enhances the applicability of AI in complex and heterogeneous clinical scenarios. However, major challenges persist, including data standardization, model interpretability, workflow integration, and algorithmic bias. Conclusions: AI has the potential to advance earlier and more personalized diagnostics for children with rare diseases. Achieving this requires multidisciplinary collaboration and careful attention to clinical, technical, and ethical considerations. Full article

Background/Objectives: Glioblastomas are a part of adult-type diffuse gliomas, the most common and most aggressive primary brain tumors in adults (glioblastoma, IDH-wildtype). The identification of the genetic factors associated with glioblastoma could be an important contribution to the diagnosis and early prevention of this disease. We compiled data from the global literature and analyzed clinically relevant variants implicated in glioblastoma risk. Methods: PubMed, Web of Science, and Scopus were used as databases. Associations between the SNPs and glioblastoma risk were calculated as a measure of pooled odds ratios (ORs) and 95% confidence intervals. Pearson’s analysis was used for epidemiological correlation (only p-values less than 0.05 were statistically significant), and data were obtained from the World Health Organization platform and the 1000 Genomes Project. Statistical analysis was performed using Review Manager (RevMan) 5.4 and BioEstat 5.0. Results: CCDC26 rs891835 G/T, G/G, and G/T-G/G genotypes were analyzed and determined to increase glioblastoma risk (G/T OR = 1.96, 95% CI: 1.38–2.77, p = 0.0002, I² = 0%; G/G OR = 1.33, 95% CI: 0.46–3.85, p = 0.60, I² = 0%; G/T − G/G OR = 1.96, 95% CI: 1.39–2.76, p = 0.0001, I² = 0%). Epidemiological correlation also demonstrated that the higher the frequency of the CCDC26 rs891835 variant, the higher the incidence of that variant in the European population. Conclusions: CCDC26 rs891835 may serve as a predictive biomarker for glioblastoma, IDH-wildtype risk and may influence higher glioblastoma incidence rates in the European population. Full article

Background: Inborn errors of immunity (IEI) are mainly genetically driven disorders that affect immune function and present with highly heterogeneous clinical manifestations, ranging from severe combined immunodeficiency (SCID) to adult-onset immune dysregulatory diseases. This clinical heterogeneity, coupled with limited awareness and the absence of a universal diagnostic test, makes early and accurate diagnosis challenging. Although genetic testing methods such as whole-exome and genome sequencing have improved detection, they are often expensive, complex, and require functional validation. Recently, artificial intelligence (AI) tools have emerged as promising for enhancing diagnostic accuracy and clinical decision-making for IEI. Methods: We conducted a systematic review of four major databases (PubMed, Scopus, Web of Science, and Embase) to identify peer-reviewed English-published studies focusing on the application of AI techniques in the diagnosis and treatment of IEI across pediatric and adult populations. Twenty-three retrospective/prospective studies and clinical trials were included. Results: AI methodologies demonstrated high diagnostic accuracy, improved detection of pathogenic mutations, and enhanced prediction of clinical outcomes. AI tools effectively integrated and analyzed electronic health records (EHRs), clinical, immunological, and genetic data, thereby accelerating the diagnostic process and supporting personalized treatment strategies. Conclusions: AI technologies show significant promise in the early detection and management of IEI by reducing diagnostic delays and healthcare costs. While offering substantial benefits, limitations such as data bias and methodological inconsistencies among studies must be addressed to ensure broader clinical applicability. Full article

Background: The genomic era has transformed not only the tools of medicine but the very logic by which we understand health and disease. Whole Exome Sequencing (WES), Clinical Exome Sequencing (CES), and Whole Genome Sequencing (WGS) have catalyzed a shift from Mendelian simplicity to polygenic complexity, from genetic determinism to probabilistic interpretation. This epistemological evolution calls into question long-standing notions of causality, certainty, and identity in clinical genomics. Yet, as the promise of precision medicine grows, so too do the tensions it generates: fragmented data, interpretative opacity, and the ethical puzzles of Variants of Uncertain Significance (VUSs) and unsolicited secondary findings. Results: Despite technological refinement, the diagnostic yield of Next-Generation Sequencing (NGS) remains inconsistent, hindered by the inherent intricacy of gene–environment interactions and constrained by rigid classificatory systems like OMIM and HPO. VUSs (neither definitively benign nor pathogenic) occupy a liminal space that resists closure, burdening both patients and clinicians with uncertainty. Meanwhile, secondary findings, though potentially life-altering, challenge the boundaries of consent, privacy, and responsibility. In both adult and pediatric contexts, genomic knowledge reshapes notions of autonomy, risk, and even personhood. Conclusions: Genomic medicine has to develop into a flexible, morally sensitive paradigm that neither celebrates certainty nor ignores ambiguity. Open infrastructures, dynamic variant reclassification, and a renewed focus on interdisciplinary and humanistic approaches are essential. Only by embracing the uncertainty intrinsic to our biology can precision medicine fulfill its promise, not as a deterministic science, but as a nuanced dialogue between genes, environments, and lived experience. Full article

Background: Genomic information is becoming integral to nursing practice, yet undergraduate curricula often provide limited opportunities to apply this knowledge in realistic settings. Objective: To evaluate the impact of a clinical simulation-based intervention on nursing students’ learning of genetic counseling, with a focus on knowledge acquisition, communication skills, and student satisfaction. Methods: A sequential mixed-methods study was conducted with 30 third-year nursing students enrolled in the elective Genetics Applied to Health Sciences. Quantitative data comprised (i) pre-/post-simulation knowledge tests, (ii) a satisfaction questionnaire, and (iii) final course grades, which were compared with grades of a cohort from the previous academic year that had no simulation component (n = 28). Qualitative insights were gathered through field notes and semi-structured interviews with six purposively selected participants. During the intervention each student rotated through the roles of genetic-counseling nurse, patient, and observer, followed by a facilitated debriefing. Results: Post-simulation knowledge scores and final course grades were significantly higher than both baseline values and the historical comparison cohort. Students reported very high satisfaction, highlighting the authenticity of the scenarios and the usefulness of immediate feedback. Qualitative analysis showed that role rotation fostered deeper understanding of counseling complexities, improved empathic communication, and bolstered self-confidence when discussing hereditary risk. Conclusions: Embedding standardized-patient simulation into undergraduate genetics courses measurably improves students’ knowledge, communication proficiency, and satisfaction. These findings support incorporating similar simulation-based learning activities to bridge the gap between theoretical genetics content and real-world nursing practice. Full article

Cassava (Manihot esculenta Crantz) is essential for global food security, especially in tropical regions. As an important genetic resource, its genetics plays a key role in crop breeding, enabling the development of more productive and pest- and disease-resistant varieties. Scientometrics, which quantitatively analyzes the production and impact of scientific research, is crucial for understanding trends in cassava genetics. This study aimed to apply bibliometric methods to conduct a scientific mapping analysis based on yearly publication trends, paper classification, author productivity, journal impact factor, keywords occurrences, and omic approaches to investigate the application of genetics to the species from 1960 to 2022. From the quantitative data analyzed, 3246 articles were retrieved from the Web of Science platform, of which 654 met the inclusion criteria. A significant increase in scientific production was observed from 1993, peaking in 2018. The first article focused on genetics was published in 1969. Among the most relevant journals, Euphytica stood out with 36 articles, followed by Genetics and Molecular Research (n = 30) and Frontiers in Plant Science (n = 25). Brazil leads in the number of papers on cassava genetics (n = 143), followed by China (n = 110) and the United States (n = 75). The analysis of major methodologies (n = 185) reveals a diversified panorama during the study period. Morpho-agronomic descriptors persisted from 1978 to 2022; however, microsatellite markers were the most widely used, with 102 records. Genomics was addressed in 87 articles, and transcriptomics in 65. By clarifying the current landscape, this study supports cassava conservation and breeding, assists in public policy formulation, and guides future research in the field. Full article

Background: Polycystic ovary syndrome (PCOS) is a complex and multifactorial disorder affecting reproductive, endocrine, and metabolic functions in women of reproductive age. While environmental and lifestyle factors play a role, increasing evidence highlights the contribution of genetic and epigenetic mechanisms to its pathogenesis. Objective: This narrative review aims to provide an updated overview of the current evidence regarding the role of genetic variants, gene expression patterns, and epigenetic modifications in the etiopathogenesis of PCOS, with a focus on their impact on ovarian function, fertility, and systemic alterations. Methods: A comprehensive search was conducted across MEDLINE, EMBASE, PubMed, Web of Science, and the Cochrane Library using MeSH terms including “PCOS”, “Genes involved in PCOS”, and “Etiopathogenesis of PCOS” from January 2015 to June 2025. The selection process followed the SANRA quality criteria for narrative reviews. Seventeen studies published in English were included, focusing on original data regarding gene expression, polymorphisms, and epigenetic changes associated with PCOS. Results: The studies analyzed revealed a wide array of molecular alterations in PCOS, including the dysregulation of SIRT and estrogen receptor genes, altered transcriptome profiles in cumulus cells, and the involvement of long non-coding RNAs and circular RNAs in granulosa cell function and endometrial receptivity. Epigenetic mechanisms such as the DNA methylation of TGF-β1 and inflammation-related signaling pathways (e.g., TLR4/NF-κB/NLRP3) were also implicated. Some genetic variants—particularly in DENND1A, THADA, and MTNR1B—exhibit signs of positive evolutionary selection, suggesting possible ancestral adaptive roles. Conclusions: PCOS is increasingly recognized as a syndrome with a strong genetic and epigenetic background. The identification of specific molecular signatures holds promise for the development of personalized diagnostic markers and therapeutic targets. Future research should focus on large-scale genomic studies and functional validation to better understand gene–environment interactions and their influence on phenotypic variability in PCOS. Full article

In the era of precision medicine, effective data visualisation plays a pivotal role in supporting clinical decision-making by translating complex, multidimensional datasets into intuitive and actionable insights. This paper explores the foundational principles of visual perception, with a specific focus on pre-attentive attributes such as colour, shape, size, orientation, and spatial position, which are processed automatically by the human visual system. Drawing from cognitive psychology and perceptual science, we demonstrate how these attributes can enhance the clarity and usability of medical visualisations, reducing cognitive load and improving interpretive speed in high-stakes clinical environments. Through detailed case studies and visual examples, particularly within the field of oncology, we highlight best practices and common pitfalls in the design of dashboards, nomograms, and interactive platforms. We further examine the integration of advanced tools—such as genomic heatmaps and temporal timelines—into multidisciplinary workflows to support personalised care. Our findings underscore that visually intelligent design is not merely an aesthetic concern but a critical factor in clinical safety, efficiency, and communication, advocating for user-centred and evidence-based approaches in the development of health data interfaces. Full article

Narrative review synthesizes the most current literature on the SARS-CoV-2 XEC variant, focusing on its genomic evolution, immune evasion characteristics, epidemiological dynamics, and public health implications. To achieve this, we conducted a structured search of the literature of peer-reviewed articles, preprints, and official surveillance data from 2023 to early 2025, prioritizing virological, clinical, and immunological reports related to XEC and its parent lineages. Defined by the distinctive spike protein mutations, T22N and Q493E, XEC exhibits modest reductions in neutralization in vitro, although current evidence suggests that mRNA booster vaccines, including those targeting JN.1 and KP.2, retain cross-protective efficacy against symptomatic and severe disease. The XEC strain of SARS-CoV-2 has drawn particular attention due to its increasing prevalence in multiple regions and its potential to displace other Omicron subvariants, although direct evidence of enhanced replicative fitness is currently lacking. Preliminary analyses also indicated that glycosylation changes at the N-terminal domain enhance infectivity and immunological evasion, which is expected to underpin the increasing prevalence of XEC. The XEC variant, while still emerging, is marked by a unique recombination pattern and a set of spike protein mutations (T22N and Q493E) that collectively demonstrate increased immune evasion potential and epidemiological expansion across Europe and North America. Current evidence does not conclusively associate XEC with greater disease severity, although additional research is required to determine its clinical relevance. Key knowledge gaps include the precise role of recombination events in XEC evolution and the duration of cross-protective T-cell responses. New research priorities include genomic surveillance in undersampled regions, updated vaccine formulations against novel spike epitopes, and long-term longitudinal studies to monitor post-acute sequelae. These efforts can be augmented by computational modeling and the One Health approach, which combines human and veterinary sciences. Recent computational findings (GISAID, 2024) point to the potential of XEC for further mutations in under-surveilled reservoirs, enhancing containment challenges and risks. Addressing the potential risks associated with the XEC variant is expected to benefit from interdisciplinary coordination, particularly in regions where genomic surveillance indicates a measurable increase in prevalence. Full article

(1) Background: Oral cancer (OC) is one of the most frequently diagnosed human cancers and remains a challenge for biologists and clinicians. More than 90% of OC cases are squamous cell carcinomas (OSCCs). Despite the use of modern diagnostic and prognostic methods, the 5-year survival rate remains unsatisfactory due to the late diagnosis of the neoplastic process and its resistance to treatment. This comprehensive review aims to present the latest literature data on the use and effectiveness of saliva as a non-invasive biomarker in patients with oral cancer. (2) Methods: The article reviews the current literature on the use of salivary omics biomarkers as an effective method in diagnosing and modifying treatment in patients with OSCC; the research corpus was acquired from the PubMed/Google/Scopus/Cochrane Library/Web of Science databases in accordance with the Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA 2020) guidelines. (3) Results: The identification of salivary omics biomarkers involved in carcinogenesis and neoplastic transformation may be a potential alternative to traditional invasive diagnostic methods. Saliva, being both an abundant reservoir of organic and inorganic components derived from epithelial cells as well as a cell-free environment, is becoming an interesting diagnostic material for studies in the field of proteomics, genomics, metagenomics, and metabolomics. (4) Conclusions: Saliva-based analysis is a modern and promising method for the early diagnosis and improvement of treatment outcomes in patients with OSCC and oral potentially malignant disorders (OPMDs), with high diagnostic, therapeutic, and prognostic potential. Full article

Background: Research concerning the genetic risk for dementia has recently been headed towards new directions. Novel findings from genome-wide association studies have highlighted the association of Alzheimer’s disease incidence with many gene polymorphisms, apart from the Apolipoprotein-E genotype. The identification of additional genetic risk factors has led to the construction of specific genetic risk scores for dementia, considering many different genetic factors and specific biological pathways related to Alzheimer’s disease. Methods: We conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis method, summarizing existing data regarding genetic risk scores for Alzheimer’s disease and dementia, in order to improve the current understanding of the genetic underpinnings of dementia. In specific, five databases (PubMed/MEDLINE, Embase, Scopus, Web of science, and Cochrane Central) were searched using the keywords “genetic risk score”, “Alzheimer’s disease”, and “dementia” with specific inclusion and exclusion criteria. Results: From the 552 articles identified, we finally included 20 studies for the qualitative analysis. These reports were classified in three different categories of genetic scores: “polygenic risk scores (PRSs)” (including 11 studies), “pathway specific polygenic risk scores (p-PRSs)” (5 studies), and “complex genetic risk scores” (4 studies). Conclusions: Existing genetic risk scores have contributed to better dementia prediction and a better understanding of the underlying pathology. Novel approaches integrating multiple polygenic risk scores might ameliorate the accuracy of genetic risk scores. The combination of polygenic risk scores that are specific to related biological pathways or relevant biomarkers is of utmost importance to achieve a better predictive ability. Full article

Light-harvesting chlorophyll a/b-binding (Lhc) proteins are integral membrane proteins that bind to pigment molecules, playing a critical role in photosynthetic processes, including light energy harvesting and transfer. To investigate the role of the Lhc gene family in cucumber (Cucumis sativus L), genome-wide identification of CsLhc gene family members and analysis of their regulatory networks were carried out using bioinformation and molecular biology research methods at Anhui Science and Technology University. The results indicated that the Lhc family consisted of 21 members, being categorized into five subfamilies: Lhca, Lhcb, CP24, CP26, and CP29. The gene structure and motifs within each subfamily are generally conserved. CsLhcs are distributed on seven chromosomes, including one pair of tandem duplicates and two pairs of segmental duplicates. Six CsLhcs exhibit eight linear relationships with seven AtLhcs, and one CsLhc shows a syntenic relationship with one OsLhc. Analysis of the cis-acting elements in CsLhc promoters revealed their potential involvement in stress responses. Transcriptome data indicated that CsLhcs are minimally expressed in male flowers and roots, but highly expressed in other organs. Analysis of stress response processes revealed that all Lhc genes participate in at least one stress response. Five Lhc genes were confirmed to appear to have expression change using qPCR analysis under high temperature and salt stress. Particularly, under downy mildew, root-knot nematode stresses, and blight stress, up-regulated Lhc genes were the most abundant ones, indicating that the Lhc family acts as a significant role in the growth and development of cucumber. These results provide valuable insights for further understanding the characteristics of the CsLhc family and analyzing the function of the Lhc family in cucumber resistance to biotic/abiotic stresses and in molecular breeding. Full article

Multimodal artificial intelligence (AI) is driving a paradigm shift in modern biomedicine by seamlessly integrating heterogeneous data sources such as medical imaging, genomic information, and electronic health records. This review explores the transformative impact of multimodal AI across three pivotal areas: biomaterials science, medical diagnostics, and personalized medicine. In the realm of biomaterials, AI facilitates the design of patient-specific solutions tailored for tissue engineering, drug delivery, and regenerative therapies. Advanced tools like AlphaFold have significantly improved protein structure prediction, enabling the creation of biomaterials with enhanced biological compatibility. In diagnostics, AI systems synthesize multimodal inputs combining imaging, molecular markers, and clinical data—to improve diagnostic precision and support early disease detection. For precision medicine, AI integrates data from wearable technologies, continuous monitoring systems, and individualized health profiles to inform targeted therapeutic strategies. Despite its promise, the integration of AI into clinical practice presents challenges such as ensuring data security, meeting regulatory standards, and promoting algorithmic transparency. Addressing ethical issues including bias and equitable access remains critical. Nonetheless, the convergence of AI and biotechnology continues to shape a future where healthcare is more predictive, personalized, and responsive. Full article

Harnessing state-of-the-art technologies to improve disease resistance is a critical objective in modern plant breeding. Artificial intelligence (AI), particularly deep learning and big model (large language model and large multi-modal model), has emerged as a transformative tool to enhance disease detection and omics prediction in plant science. This paper provides a comprehensive review of AI-driven advancements in plant disease detection, highlighting convolutional neural networks and their linked methods and technologies through bibliometric analysis from recent research. We further discuss the groundbreaking potential of large language models and multi-modal models in interpreting complex disease patterns via heterogeneous data. Additionally, we summarize how AI accelerates genomic and phenomic selection by enabling high-throughput analysis of resistance-associated traits, and explore AI’s role in harmonizing multi-omics data to predict plant disease-resistant phenotypes. Finally, we propose some challenges and future directions in terms of data, model, and privacy facets. We also provide our perspectives on integrating federated learning with a large language model for plant disease detection and resistance prediction. This review provides a comprehensive guide for integrating AI into plant breeding programs, facilitating the translation of computational advances into disease-resistant crop breeding. Full article

Edible fungi, a group of globally significant macrofungi, are highly valued for their unique flavors and substantial nutritional and medicinal properties. Understanding the molecular mechanisms governing their growth, development, gene function, biosynthesis of valuable compounds, and environmental adaptation is crucial for enhancing yield and quality, providing essential scientific support for industrial progress. Genomics, transcriptomics, and proteomics, as cornerstone life science technologies, offer powerful, integrated approaches to decipher genetic codes, reveal gene expression patterns, and elucidate complex metabolic networks in edible fungi. These advancements are transitioning research from traditional cultivation methods towards deeper molecular biology exploration. This review synthesizes key progress in applying genomics, transcriptomics, and proteomics to edible fungi, with a particular focus on metabolism-related research and the fundamentals of metabolic network construction. It discusses how these technologies, independently and in preliminary integration, uncover critical steps and regulatory mechanisms within endogenous metabolic pathways. While acknowledging the importance of metabolomics and epigenomics as cutting-edge areas, this review focuses on the “classical triad” of genomics, transcriptomics, and proteomics due to their technological maturity, data accessibility, and established application base in elucidating core metabolic mechanisms in edible fungi. The goal is to deepen the understanding of edible fungi metabolic mechanisms, providing a vital theoretical basis and practical insights for optimizing cultivation, enabling genetic improvement, harnessing bioactive substances, and promoting industrial upgrading, thereby boosting the overall efficiency and competitiveness of the edible fungi industry. Full article