Search Results (520)

This perspective paper critically examines the emerging role of voxel-based analysis (VBA), also referred to as image-based data mining (IBDM), in dose–toxicity modeling for post-radiotherapy toxicity assessment. These techniques offer promising insights into localized organ subregions associated with toxicity, yet their current application faces substantial methodological and validation challenges. Based on prior studies and practical experience, we highlight seven key limitations: (i) lack of clinical validation for dose–toxicity models, (ii) strong dependence of results on statistical method selection (parametric vs. nonparametric), (iii) insensitivity of commonly used tests to uniform dose scaling, (iv) influence of tail selection (one- vs. two-tailed tests) on statistical power, (v) frequent misapplication of permutation testing, (vi) reliance on dose as the sole predictor while neglecting patient-, treatment-, and genomic-level covariates, and (vii) misinterpretation of voxel-wise associations as causal in the absence of appropriate causal inference frameworks. Collectively, these limitations can obscure clinically relevant dose differences, inflate false-positive or false-negative findings, obscure effect direction, introduce confounded associations, and ultimately yield inconsistent identification of high-risk subregions in organs at risk and poor reproducibility across studies. Notably, current univariable VBA/IBDM approaches should be regarded as hypothesis-generating rather than clinical decision-making tools, as unvalidated findings risk premature translation into clinical practice. Advancing personalized radiotherapy requires rigorous outcome validation, integration of multivariable and causal modeling strategies, and incorporation of clinical and genomic data. By moving beyond dose-only predictor models, VBA/IBDM can achieve greater biological relevance, reliability, and clinical utility, supporting more precise and individualized radiotherapy strategies. Full article

The European mink Mustela lutreola is one of the most threatened carnivores in Europe, having suffered dramatic range contractions and severe population fragmentation. Accurate knowledge of its genetic diversity is crucial for conservation planning, yet earlier studies based on partial mitochondrial markers offered limited resolution and often underestimated haplotype richness. In this study, complete mitochondrial genomes from four extant populations (Russia, n = 11; Romania, n = 16; Germany, n = 24; France–Spain, n = 15) were analysed using a suite of non-parametric and asymptotic estimators (Fisher’s α, ACE, Jackknife1, Bootstrap, Chao1-based iNEXT) together with negative binomial modelling. A total of 41 haplotypes were detected, but extrapolated estimates indicated substantially higher richness, particularly in populations dominated by singletons. Rarefaction and extrapolation analyses revealed that sample sizes of 70–130 individuals per population are needed to approach complete haplotype detection. The France–Spain and Romania populations harboured the highest predicted richness, whereas Germany and Russia, both represented by ex situ stocks, showed lower diversity. These results refine earlier assumptions of extreme homogeneity in the Western population and demonstrate that significant mitochondrial variation persists at the continental scale. The study provides quantitative benchmarks for sampling design and genetic management, supporting preservation of evolutionary potential in this critically endangered species. Full article

Background/Objectives: Appendiceal cancer (AC) is a rare and understudied malignancy with limited genomic data available to guide clinical interventions. Historically treated as a subtype of colorectal cancer, AC is now recognized as a distinct disease with unique histologic subtypes and molecular features. This review aims to consolidate current genomic data across AC subtypes and explore the clinical relevance of recurrent mutations. Methods: A systematic literature review was performed in accordance with general Preferred Reporting Items for Systemic Reviews and Meta-Analyses (PRISMA) guidelines. Using search engines such as PubMed and Web of Science, we selected studies based on relevance to AC genomics using search terms such as “appendix cancer”, “appendiceal cancer”, “pseudomyxoma peritonei”, “sequencing”, “mutation”, and “genotype”. Results: AC comprises five major histologic subtypes—appendiceal neuroendocrine neoplasms (ANENs), mucinous appendiceal neoplasms (MANs), goblet cell adenocarcinomas (GCAs), colonic-type adenocarcinomas (CTAs) and signet ring cell adenocarcinomas (SRCs)—each with unique clinical behaviors and mutational profiles. Low-grade tumors, such as ANENs and MANs, frequently harbor KRAS and GNAS mutations, while high-grade subtypes, such as CTAs and SRCs, are enriched for TP53, APC, and SMAD gene alterations. GCA tumors exhibit a distinct mutational spectrum involving chromatin remodeling genes such as ARID1A and KMT2D. Compared to colorectal cancer, AC demonstrates lower frequencies of APC and TP53 mutations and a higher prevalence of GNAS mutations, consistent with a pathological divergence from CRC. Conclusions: The genomic heterogeneity of AC is commensurate with its histological complexity and has important implications for diagnosis, prognosis and treatment. While certain actionable mutations are present in a subset of tumors, large-scale genomic characterization efforts and development of subtype-specific models will be essential for advancing precision medicine in AC. Full article

Type 1 Diabetes Mellitus (T1D) is an autoimmune disease characterized by the destruction of pancreatic β-cells, predominantly manifesting in childhood or adolescence. The lack of clearly interpretable biological markers in the early stages, combined with the insidious onset of the disease, poses significant challenges to early diagnosis and the implementation of preventive strategies. The applicability of classic T1D biomarkers for understanding the mechanisms of the autoimmune process, preclinical diagnostics and treatment efficiency is limited. Despite advances in next-generation sequencing (NGS) technologies, which have enabled large-scale genome-wide association studies (GWASs) and the identification of polygenic risk scores (PRSs) associated with T1D predisposition, as well as progress in bioinformatics approaches for assessing dysregulated gene expression, no universally accepted risk assessment model or definitive predictive biomarker has been established. Until now, the use of new promising biomarkers for T1D diagnostics is limited by insufficient evidence base. However, they have great potential for the development of diagnostic methods on their basis, which has been shown in single or serial large-scale studies. This critical review covers both well-known biomarkers widely used in clinical practice, such as HLA-haplotype, non-HLA SNPs, islet antigen autoantibodies, C-peptide, and the promising ones, such as cytokines, cfDNA, microRNA, T1D-specific immune cells, islet-TCR, and T1D-specific vibrational bands. Additionally, we highlight new approaches that have been gaining popularity and have already demonstrated their potential: GWAS, single-cell transcriptomics, identification of antigen-specific T cells using scRNA-seq, and FTIR spectroscopy. Although some of the biomarkers, in our opinion, are still limited to a research context or are far from being implemented in clinical diagnostics of T1D, they have the greatest potential of being applied in clinical practice. When integrated with the monitoring of the classical autoimmune diabetes markers, they would increase the sensitivity and specificity during diagnostics of early and preclinical stages of the disease. This critical review aims to evaluate the current landscape of classical and emerging biomarkers in autoimmune diabetes, with a focus on those enabling early detection—prior to extensive destruction of pancreatic islets. Another goal of the review is to focus the attention of the scientific community on the gaps in early T1D diagnostics, and to help in the selection of markers, targets, and methods for scientific studies on creating novel diagnostic panels. Full article

Vasconcellea pubescens (mountain papaya) is an underutilized Andean fruit with distinctive nutritional and functional properties, yet its rapid softening and short shelf-life result in significant postharvest losses. This review summarizes current knowledge on the physiology of fruit development and ripening, with emphasis on cell wall disassembly, color changes, and ethylene regulation as determinants of postharvest quality. Advances in postharvest management strategies, including temperature control, packaging, and ethylene-modulating treatments (such as 1-MCP), are discussed in the context of preserving fruit firmness, extending shelf life, and reducing food waste. Furthermore, the high content of bioactive compounds—such as papain, phenolics, and flavonoids—underscores the potential of valorizing by-products through sustainable biotechnological applications. Despite recent progress, critical gaps remain in genomic resources, predictive quality monitoring, and large-scale implementation of preservation techniques. Addressing these challenges could enhance the economic and ecological value of V. pubescens, positioning it as both a model species for postharvest research and a promising fruit for reducing food losses in horticultural supply chains. Full article

Aphids remain exceptional models for symbiosis research due to their unique experimental advantages that extend beyond documenting symbiont-mediated phenotypes. Nine commonly occurring facultative bacterial symbionts provide well-characterized benefits, including defense against parasitoids, pathogens, and thermal stress. Yet the system’s greatest value lies in enabling diverse research applications across biological disciplines through experimental tractability combined with ecological realism. Researchers can create controlled experimental lines through symbiont manipulation, maintain clonal host populations indefinitely, and cultivate symbionts independently. This experimental power is complemented by extensive knowledge of symbiont dynamics in natural populations, including temporal and geographic distribution patterns—features generally unavailable in other insect-microbe systems. These advantages facilitate investigation of key processes in symbiosis, including transmission dynamics, mechanisms, strain-level functional diversity, multi-partner infections, and transitions from facultative to co-obligate relationships. Integration across biological scales—from genomics to field ecology—enables research on symbiont community assembly, ecological networks, coevolutionary arms races, and agricultural applications. This combination of experimental flexibility, comprehensive natural history knowledge, and applied relevance positions aphids as invaluable for advancing symbiosis theory while addressing practical challenges in agriculture and invasion biology. Full article

Background: Facing global wheat production pressures such as environmental degradation and reduced cultivated land, breeding innovation is urgent to boost yields. Genomic selection (GS) is a useful wheat breeding technology to make the breeding process more efficient, increasing the genetic gain per unit time and cost. Precise genomic estimated breeding value (GEBV) via genome-wide markers is usually hampered by high-dimensional genomic data. Methods: To address this, we propose GRE, a framework combining genome-wide association study (GWAS)’s biological significance and random forest (RF)’s prediction efficiency for an explainable machine learning GS model. First, GRE identifies significant SNPs affecting wheat yield traits by comparison of the constructed 24 SNP subsets (intersection/union) selected by leveraging GWAS and RF, to analyze the marker scale’s impact. Furthermore, GRE compares six GS algorithms (GBLUP and five machine learning models), evaluating performance via prediction accuracy (Pearson correlation coefficient, PCC) and error. Additionally, GRE leverages Shapley additive explanations (SHAP) explainable techniques to overcome traditional GS models’ “black box” limitation, enabling cross-scale quantitative analysis and revealing how significant SNPs affect yield traits. Results: Results show that XGBoost and ElasticNet perform best in the union (383 SNPs) of GWAS and RF’s TOP 200 SNPs, with high accuracy (PCC > 0.864) and stability (standard deviation, SD < 0.005), and the significant SNPs identified by XGBoost are precisely explained by their main and interaction effects on wheat yield by SHAP. Conclusions: This study provides tool support for intelligent breeding chip design, important trait gene mining, and GS technology field transformation, aiding global agricultural sustainable productivity. Full article

Event-based models (EBMs) are powerful tools for inferring probabilistic sequences of monotonic biomarker changes in progressive diseases, but their use is often hindered by data quality issues, high dimensionality, and limited interpretability. We introduce ReduXis, a streamlined pipeline that overcomes these challenges via three key innovations. First, upon dataset upload, ReduXis performs an automated data readiness assessment—verifying file formats, metadata completeness, column consistency, and measurement compatibility—while flagging preprocessing errors, such as improper scaling, and offering actionable feedback. Second, to prevent overfitting in high-dimensional spaces, ReduXis implements an ensemble voting-based feature selection strategy, combining gradient boosting, logistic regression, and random forest classifiers to identify a robust subset of biomarkers. Third, the pipeline generates interpretable outputs—subject-level staging and subtype assignments, comparative biomarker profiles across disease stages, and classification performance visualizations—facilitating transparency and downstream analysis. We validate ReduXis on three diverse cohorts: the Emory Healthy Brain Study (EHBS) cohort of patients with Alzheimer’s disease (AD), a Genomic Data Commons (GDC) cohort of transitional cell carcinoma (TCC) patients, and a GDC cohort of colorectal adenocarcinoma (CRAC) patients. Full article

The rapid evolution of artificial intelligence (AI) and machine learning (ML) technologies has initiated a paradigm shift in contemporary spine care. This narrative review synthesizes advances across imaging-based diagnostics, surgical planning, genomic risk stratification, and post-operative outcome prediction. We critically assess high-performing AI tools, such as convolutional neural networks for vertebral fracture detection, robotic guidance platforms like Mazor X and ExcelsiusGPS, and deep learning-based morphometric analysis systems. In parallel, we examine the emergence of ambient clinical intelligence and precision pharmacogenomics as enablers of personalized spine care. Notably, genome-wide association studies (GWAS) and polygenic risk scores are enabling a shift from reactive to predictive management models in spine surgery. We also highlight multi-omics platforms and federated learning frameworks that support integrative, privacy-preserving analytics at scale. Despite these advances, challenges remain—including algorithmic opacity, regulatory fragmentation, data heterogeneity, and limited generalizability across populations and clinical settings. Through a multidimensional lens, this review outlines not only current capabilities but also future directions to ensure safe, equitable, and high-fidelity AI deployment in spine care delivery. Full article

Nanopore-based DNA sequencing has emerged as a transformative biosensing technology, enabling real-time molecular diagnostics in compact and mobile form factors. However, the computational complexity of the basecalling process—the step that translates raw nanopore signals into nucleotide sequences—poses a critical energy challenge for mobile deployment. While deep learning (DL) models currently dominate this task due to their high accuracy, they demand substantial power budgets and computing resources, making them unsuitable for portable or field-scale biosensor platforms. In this work, we propose an embedded hardware–software framework for DNA sequence detection that leverages a Viterbi-based Hidden Markov Model (HMM) implemented on a custom 64-bit RISC-V core. The proposed HMM detector is realized on an off-the-shelf Virtex-7 FPGA and evaluated against state-of-the-art DL-based basecallers in terms of energy efficiency and inference accuracy. From one side, the experimental results show that our system achieves an energy efficiency improvement of 6.5×, 5.5×, and 4.6×, respectively, compared to similar HMM-based detectors implemented on a commodity x86 processor, Cortex-A9 ARM embedded system, and a previously published Rocket-based system. From another side, the proposed detector demonstrates 15× and 2.4× energy efficiency superiority over state-of-the-art DL-based detectors, with competitive accuracy and sufficient throughput for field-based genomic surveillance applications and point-of-care diagnostics. This study highlights the practical advantages of classical probabilistic algorithms when tightly integrated with lightweight embedded processors for biosensing applications constrained by energy, size, and latency. Full article

This paper presents a hybrid computational architecture for efficient and robust digital transmission inspired by helical genetic structures. The proposed system integrates advanced modulation schemes, such as multi-pulse-position modulation (MPPM), high-order quadrature amplitude modulation (QAM), and chirp spread spectrum (CSS), along with Reed–Solomon error correction and quantum-assisted search, to optimize performance in noisy and non-line-of-sight (NLOS) optical environments, including VLC channels modeled with log-normal fading. Through mathematical modeling and simulation, we demonstrate that the number of helical transmissions required for genome-scale data can be drastically reduced—up to 95% when using parallel strands and high-order modulation. The trade-off between redundancy, spectral efficiency, and error resilience is quantified across several configurations. Furthermore, we compare classical genetic algorithms and Grover’s quantum search algorithm, highlighting the potential of quantum computing in accelerating decision-making and data encoding. These results contribute to the field of operations research and supply chain communication by offering a scalable, energy-efficient framework for data transmission in distributed systems, such as logistics networks, smart sensing platforms, and industrial monitoring systems. The proposed architecture aligns with the goals of advanced computational modeling and optimization in engineering and operations management. Full article

Forest soil greenhouse gas emissions play a critical role in global climate change. This review synthesizes the mechanisms of temperature change impacts on forest soil greenhouse gas (CO₂, CH₄, N₂O) emissions, the complex response patterns of ecosystems, and existing knowledge gaps in current research. We highlight several critical mechanisms, such as the high temperature sensitivity (Q₁₀) of methane (CH₄) and CO₂ emissions from high-latitude peatlands, and the dual effect of chronic nitrogen deposition, which can cause short-term stimulation but long-term suppression of soil CO₂ emissions. It emphasizes how climatic factors, soil characteristics, vegetation types, and anthropogenic disturbances (such as forest management and fire) regulate emission processes through multi-scale interactions. This review further summarizes the advancements and limitations of current research methodologies and points out future research directions. These include strengthening long-term multi-factor experiments, developing high-precision models that integrate microbial functional genomics and isotope tracing techniques, and exploring innovative emission reduction strategies. Ultimately, this synthesis aims to provide a scientific basis and key ecological threshold references for developing climate-resilient sustainable forest management practices and effective climate change mitigation policies. Full article

Per- and polyfluoroalkyl substances (PFASs) are widespread in the environment, bioaccumulate in humans, and lead to disease and organ injury, such as liver steatosis. However, we lack a clear understanding of how these chemicals cause organ-level toxicity. Here, we aimed to analyze PFAS-induced metabolic perturbations in male and female rat livers by combining a genome-scale metabolic model (GEM) and toxicogenomics. The combined approach overcomes the limitations of the individual methods by taking into account the interaction between multiple genes for metabolic reactions and using gene expression to constrain the predicted mechanistic possibilities. We obtained transcriptomic data from an acute exposure study, where male and female rats received a daily PFAS dose for five consecutive days, followed by liver transcriptome measurement. We integrated the transcriptome expression data with a rat GEM to computationally predict the metabolic activity in each rat’s liver, compare it between the control and PFAS-exposed rats, and predict the benchmark dose (BMD) at which each chemical induced metabolic changes. Overall, our results suggest that PFAS-induced metabolic changes occurred primarily within the lipid and amino acid pathways and were similar between the sexes but varied in the extent of change per dose based on sex and PFAS type. Specifically, we identified that PFASs affect fatty acid-related pathways (biosynthesis, oxidation, and sphingolipid metabolism), energy metabolism, protein metabolism, and inflammatory and inositol metabolite pools, which have been associated with fatty liver and/or insulin resistance. Based on these results, we hypothesize that PFAS exposure induces changes in liver metabolism and makes the organ sensitive to metabolic diseases in both sexes. Furthermore, we conclude that male rats are more sensitive to PFAS-induced metabolic aberrations in the liver than female rats. This combined approach using GEM-based predictions and BMD analysis can help develop mechanistic hypotheses regarding how toxicant exposure leads to metabolic disruptions and how these effects may differ between the sexes, thereby assisting in the metabolic risk assessment of toxicants. Full article

Recent advances in computational biology have provided powerful tools for analyzing, modeling, and optimizing probiotic microorganisms, thereby supporting their development as promising agents for improving human health. The essential role of the microbiota in regulating physiological processes and preventing disease has driven interest in the rational design of next-generation probiotics. This review highlights progress in in silico approaches for enhancing the functionality of probiotic strains. Particular attention is given to genome-scale metabolic models, advanced simulation algorithms, and AI-driven tools that provide deeper insight into microbial metabolism and enable precise probiotic optimization. The integration of these methods with multi-omics data has greatly improved our ability to predict strain behavior and design probiotics with specific health benefits. Special focus is placed on modeling probiotic–prebiotic interactions and host–microbiome dynamics, which are essential for the development of functional food products. Despite these achievements, key challenges remain, including limited model accuracy, difficulties in simulating complex host–microbe systems, and the absence of unified standards for validating in silico-optimized strains. Addressing these gaps requires the development of integrative modeling platforms and clear regulatory frameworks. This review provides a critical overview of current advances, identifies existing barriers, and outlines future directions for the application of computational strategies in probiotic research. Full article