Saved Queries

by Aoife Burke, Emma O’ Sullivan, Linda Giblin, Anne P. Nugent, Albert Flynn, Breige A. McNulty, Laura Kehoe, Michael Callanan and Janette Walton

Nutrients 2026, 18(3), 487; https://doi.org/10.3390/nu18030487 (registering DOI) - 2 Feb 2026

Abstract

Background/Objectives: The global population is ageing rapidly, with projections indicating that there will be over two billion individuals aged ≥60 years by 2050. Sarcopenia and frailty are major age-related syndromes associated with loss of muscle mass, reduced strength, and increased vulnerability, for which adequate protein and amino acid intake are key preventive factors. However, nationally representative data on dietary amino acid intakes and sources among older adults are lacking, particularly in Europe. Methods: This study aimed to address this gap by updating the Irish Food Composition Database (IFCD) (2011) with amino acid composition data and estimating amino acid intakes and dietary sources in older adults in Ireland (≥65 years) using data from the National Adult Nutrition Survey (2008–2010; n = 226). Results: Mean total amino acid intake was 76.2 g/day (1.0 g/kg body weight/day). Intakes of all essential amino acids were above the US Institute of Medicine (IOM) recommendations, with no significant differences observed between sexes or age groups (65–74 y, 75+ y). ‘Meat and meat dishes’ were the principal contributors to amino acid intake (28–47%), followed by ‘breads and rolls’, ‘milk and yoghurt’, and ‘fish and fish dishes’. Conclusions: This study provides the first nationally representative estimates of amino acid intakes in older adults in Europe, establishing a baseline for future dietary surveillance and informing protein quality assessment amid dietary transitions toward plant-based foods. Full article

(This article belongs to the Section Proteins and Amino Acids)

41 pages, 22538 KB

Open AccessArticle

IALA: An Improved Artificial Lemming Algorithm for Unmanned Aerial Vehicle Path Planning

by Xiaojun Zheng, Rundong Liu, Shiming Huang and Zhicong Duan

Technologies 2026, 14(2), 91; https://doi.org/10.3390/technologies14020091 (registering DOI) - 1 Feb 2026

Abstract

With the increasing application of unmanned aerial vehicle (UAV) in multiple fields, the path planning problem has become a key challenge in the optimization domain. This paper proposes an Improved Artificial Lemming Algorithm (IALA), which incorporates three strategies: the optimal information retention strategy based on individual historical memory, the hybrid search strategy based on differential evolution operators, and the local refined search strategy based on directed neighborhood perturbation. These strategies are designed to enhance the algorithm’s global exploration and local exploitation capabilities in tackling complex optimization problems. Subsequently, comparative experiments are conducted on the CEC2017 benchmark suite across three dimensions (30D, 50D, and 100D) against eight state-of-the-art algorithms proposed in recent years, including SBOA and DBO. The results demonstrate that IALA achieves superior performance across multiple metrics, ranking first in both the Wilcoxon rank-sum test and the Friedman ranking test. Analyses of convergence curves and data distributions further verify its excellent optimization performance and robustness. Finally, IALA and the comparative algorithms are applied to eight 3D UAV path planning scenarios and two amphibious UAV path planning models. In the independent repeated experiments across the eight scenarios, IALA attains the optimal performance 13 times in terms of the two metrics, Mean and Std. It also ranks first in the Monte Carlo experiments for the two amphibious UAV path planning models. Full article

(This article belongs to the Section Information and Communication Technologies)

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21 pages, 10040 KB

Open AccessArticle

Design of Monitoring System for River Crab Feeding Platform Based on Machine Vision

by Yueping Sun, Ziqiang Li, Zewei Yang, Bikang Yuan, De’an Zhao, Ni Ren and Yawen Cheng

Fishes 2026, 11(2), 88; https://doi.org/10.3390/fishes11020088 (registering DOI) - 1 Feb 2026

Abstract

Bait costs constitute 40–50% of the total expenditure in river crab aquaculture, highlighting the critical need for accurately assessing crab growth and scientifically determining optimal feeding regimes across different farming stages. Current traditional methods rely on periodic manual sampling to monitor growth status and artificial feeding platforms to observe consumption and adjust bait input. These approaches are inefficient, disruptive to crab growth, and fail to provide comprehensive growth data. Therefore, this study proposes a machine vision-based monitoring system for river crab feeding platforms. Firstly, the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm is applied to enhance underwater images of river crabs. Subsequently, an improved YOLOv11 (You Only Look Once) model is introduced and applied for multi-target detection and counting in crab ponds, enabling the extraction of information related to both river crabs and bait. Concurrently, underwater environmental parameters are monitored in real-time via an integrated environmental information sensing system. Finally, an information processing platform is established to facilitate data sharing under a “detection–processing–distribution” workflow. The real crab farm experimental results show that the river crab quality error rate was below 9.57%, while the detection rates for both corn and pellet baits consistently exceeded 90% across varying conditions. These results indicate that the proposed system significantly enhances farming efficiency, elevates the level of automation, and provides technological support for the river crab aquaculture industry. Full article

(This article belongs to the Section Fishery Facilities, Equipment, and Information Technology)

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30 pages, 8655 KB

Open AccessArticle

GAN-MIGA-Driven Building Energy Prediction and Block Layout Optimization: A Case Study in Lanzhou, China

by Xinwei Guo, Shida Wang and Jingyi Li

Urban Sci. 2026, 10(2), 77; https://doi.org/10.3390/urbansci10020077 (registering DOI) - 1 Feb 2026

Abstract

With the rapid urbanization in China, building energy consumption has become a critical challenge for sustainable urban development. Conventional simulation methods are computationally intensive and inefficient for large-scale urban layout optimization, highlighting the need for fast and reliable predictive approaches. Existing machine learning models often overlook spatial relationships among buildings and rely heavily on manual feature engineering, which limits their applicability at the urban block scale. To address these limitations, the study proposes a building energy consumption prediction model for urban blocks based on Generative Adversarial Networks (GANs), which preserves spatial information while significantly advancing computational speed. The optimal GAN model is further integrated with a Multi-Island Genetic Algorithm (MIGA) to form a GAN-MIGA optimization framework, which is applied to the layout optimization of a target urban block in Lanzhou. Key findings include: (1) the GAN model achieves an average prediction error of 6.8% compared with conventional energy simulations; (2) the GAN-MIGA framework reduces energy consumption by 48.78% relative to the worst-performing solution and by 22.53% compared with the original block layout; (3) the spatial distribution patterns of energy consumption predicted by the GAN are consistent with those obtained from traditional simulation methods; (4) the regression model derived from GAN-MIGA optimization results achieves an R² value exceeding 0.84; and (5) building layout design strategies are formulated based on key morphological indicators in the regression model. Overall, this study demonstrates the effectiveness of the GAN-based method for urban scale building energy prediction and layout optimization. The proposed GAN-MIGA framework provides practical tools and theoretical support for energy-efficient design, policy formulation, and smart city development, contributing to more sustainable urban energy planning. Full article

(This article belongs to the Special Issue Optimizing Urban Building Energy Analysis and Prediction: Evidence from AI, Big Data, and Machine Learning)

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18 pages, 1081 KB

Open AccessData Descriptor

Controlled Generation of Synthetic Spanish Texts: A Dataset Using LLMs with and Without Contextual Retrieval

by José M. García-Campos, Agustín W. Lara-Romero, Vicente Mayor and Jorge Calvillo-Arbizu

Data 2026, 11(2), 29; https://doi.org/10.3390/data11020029 (registering DOI) - 1 Feb 2026

Abstract

The increasing ability of Large Language Models (LLMs) to generate fluent and coherent text has heightened the need for resources to analyze and detect synthetic content, particularly in Spanish, where the scarcity of datasets hinders the development of reliable detection systems. This work presents a Spanish-language dataset of 18,236 synthetic news descriptions generated from real journalistic headlines using a fully reproducible, open-source pipeline. The methodology used to produce the dataset includes both a Retrieval Augmented Generation (RAG) approach, which incorporates contextual information from recent news descriptions, and a NO-RAG approach, which relies solely on the headline. Texts were generated with the instruction-tuned Mistral 7B Instruct model, systematically varying temperature to explore the effect of generation parameters. The dataset includes detailed metadata linking each synthetic description to its source headline, generation settings, and, when applicable, retrieved contextual content. By combining contextual grounding, controlled parameter variation, and source-level traceability, this dataset provides a reproducible and richly annotated resource that supports research in Spanish synthetic text and evaluation of LLM-based generation. Full article

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14 pages, 310 KB

Open AccessArticle

Cytokine Dynamics in Severe COVID-19 vs. Influenza A Elderly Patients: A Prospective Comparative Study

by Mihai Aronel Rus, Adina Huțanu, Daniel Corneliu Leucuța, Violeta Tincuța Briciu, Monica Iuliana Muntean, Angela Ionică and Mihaela Sorina Lupșe

Int. J. Mol. Sci. 2026, 27(3), 1463; https://doi.org/10.3390/ijms27031463 (registering DOI) - 1 Feb 2026

Abstract

COVID-19 and influenza A (FluA) cause severe respiratory infections in elderly patients, with cytokine dysregulation playing a central role. Direct comparative data in older adults remains limited. We aimed to characterize cytokine dynamics and their prognostic value in hospitalized elderly patients with COVID-19 vs. FluA. We performed a prospective cohort study including adults ≥ 60 years hospitalized with respiratory failure due to COVID-19 or FluA between March 2023 and March 2024. Serum IL-1β, IL-6, IL-10, IL-17A, IL-34, MCP-1, and CXCL10 were measured on Day 1 and Day 5 of hospitalization using Luminex^®. Cytokines and associations with non-invasive ventilation (NIV) were assessed by ROC analysis and multivariate logistic regression. 83 patients were included (39 COVID-19, median age 79 years; 44 FluA, median 77 years). At Day 1, COVID-19 exhibited significantly higher IL-6, IL-10, and CXCL10; FluA showed an attenuated cytokine response. At Day 5, cytokines declined in both groups. Baseline IL-6 independently predicted NIV (adjusted OR 3.02), whereas higher MCP-1 was associated with reduced NIV requirement. Early cytokine differences between COVID-19 and FluA are evident in elderly patients, but values converged by Day 5. IL-6 remains an informative early predictor of respiratory deterioration; MCP-1 may reflect a regulated innate response. Full article

(This article belongs to the Special Issue Molecular Research and Insights into COVID-19: Third Edition)

14 pages, 457 KB

Open AccessReview

Density Functional Theory and Information-Theoretic Diagnostics of Quantum Phase Transitions

by E. Romera and Á. Nagy

Entropy 2026, 28(2), 170; https://doi.org/10.3390/e28020170 (registering DOI) - 1 Feb 2026

Abstract

Within density functional theory (DFT), where the density is the fundamental variable, quantum phase transitions (QPTs) can be formulated through a Hamiltonian

\hat{H} = {\hat{H}}_{0} + \sum_{i} ξ_{i} {\hat{A}}_{i}

, such that the control parameters [...] Read more.

Within density functional theory (DFT), where the density is the fundamental variable, quantum phase transitions (QPTs) can be formulated through a Hamiltonian

\hat{H} = {\hat{H}}_{0} + \sum_{i} ξ_{i} {\hat{A}}_{i}

, such that the control parameters

{ξ_{i}}

are in bijective correspondence (in the nondegenerate case) with the “densities”

a_{i} = ⟨ {\hat{A}}_{i} ⟩

, and the functional

Q ({a_{i}})

acts as the Legendre transform of the energy; this structure even permits the use of Rényi entropy (for a given order) as an alternative control parameter, while degeneracy can be handled via a subspace density. On this foundation, information-theoretic measures provide sensitive diagnostics of criticality: fidelity and its susceptibility

χ

, Fisher information, relative Rényi entropy, and the Kullback–Leibler divergence are locally linked by

R^{q} \approx q I_{K L} \approx 2 q χ {(δ λ)}^{2}

, revealing their proportionality in the small-parameter-shift regime. Applied to the Dicke model, numerical analyses show that fidelity exhibits pronounced curvature or divergence near

λ_{c} = \sqrt{ω ω_{0}} / 2

and that the response sharpens with increasing j, corroborating that these information measures capture QPTs with precision within the DFT framework. Full article

(This article belongs to the Special Issue The Information-Theoretic Approach in Density Functional Theory and Beyond)

19 pages, 1539 KB

Open AccessArticle

Effects of Fertigation Programs and Substrates on Growth, Fruit Quality, and Yield of Bell Pepper (Capsicum annuum) in Greenhouse Conditions

by Ángel R. Pimentel-Pujols, José M. García, Fernando Borrás and Juana Fernández-López

Foods 2026, 15(3), 505; https://doi.org/10.3390/foods15030505 (registering DOI) - 1 Feb 2026

Abstract

Global vegetable production exceeded 1.2 billion tons in 2022, with bell pepper (Capsicum annuum) accounting for 37 million tons, a crop of high value due to its versatility, commercial demand, and nutritional properties. In the Dominican Republic, greenhouse vegetable production has experienced accelerated growth over the last 23 years, reaching over 10 million m² of infrastructure and increasing pepper production from 9122 to 32,000 tons. However, limitations in technical information regarding nutritional management and substrate use persist, despite the extensive empirical experience of producers and technicians. This study evaluated the effect of three fertigation programs (low, medium, and high doses: FP1, FP2, and FP3) and three substrates (carbonized rice husk- CRH, coconut fiber-CF, and a 1:1 Mix) on 180 plants grown for 141 days in a greenhouse, using a completely randomized split-plot design. Growth, physiological, quality, and yield indicators were measured. Principal Component Analysis (PCA) explained 88% of the variability, showing that FP2 and FP3, combined with BRH and the 1:1 Mix, generated greater plant height, stem diameter, chlorophyll content, and canopy development, while FP1 and CF were associated with lower performance. Regarding fruit quality, the BRH and 1:1 Mix substrates yielded higher values for length, width, and weight, whereas °Brix content responded primarily to fertigation doses. Total yield confirmed this pattern, highlighting FP3–BRH as the best combination evaluated and FP1–CF as the one with the lowest productivity. Full article

(This article belongs to the Special Issue The 6th International Electronic Conference on Foods—“Future Horizons in Foods and Sustainability” (Foods 2025))

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18 pages, 7386 KB

Open AccessArticle

Absence of Toll-like Receptor 21 (TLR21) Gene in the Genome of Transparent Glass Catfish (Kryptopterus vitreolus)

by Shengtao Guo, Xinhui Zhang, Rusong Zhang, Kai Zhang, Jianchao Chen, Yunyun Lv, Zhengyong Wen, Jieming Chen, Chao Bian and Qiong Shi

Biology 2026, 15(3), 263; https://doi.org/10.3390/biology15030263 (registering DOI) - 1 Feb 2026

Abstract

This study investigates the genomic basis of immune adaptation in the transparent glass catfish (Kv: Kryptopterus vitreolus), focusing on the loss of the Toll-like receptor 21 (TLR21) gene. Comparative genomic analysis with closely related non-transparent North African catfish (Cg: Clarias gariepinus) revealed 11 TLR genes in the latter, while only 8 TLR genes (KvTLR1, 2, 3, 5, 7, 9, 13, and 20) were retained in the glass catfish, with TLR21 specifically absent. Collinearity analysis confirmed that the genomic region containing TLR21 is conserved across eight siluriform species, with loss exclusively in the glass catfish, supporting its lineage-specific absence. Structural expansion was notable in KvTLR5, KvTLR7, and KvTLR20. Molecular docking indicated that binding stability between CpG oligonucleotides and TLR21 varies significantly, with CpG-B 1681 showing the strongest interaction, which highlights sequence-dependent ligand recognition. Interestingly, absence of the TLR1 gene in another transparent teleost, the X-ray tetra (Pristella maxillaris), suggests that transparent fishes may share an evolutionary trend of lineage-specific TLR gene loss. Together, these findings reveal a distinctive evolutionary trajectory in the innate immune receptor family of transparent fishes and provide new molecular insights into their adaptive immune strategies. These insights will benefit the academic community by improving comparative frameworks for fish innate immunity, and they may inform disease prevention and health management strategies in aquaculture and the ornamental fish trade. Full article

(This article belongs to the Special Issue Research Advances in Aquatic Omics)

19 pages, 3535 KB

Open AccessArticle

Study of Weak-Acid-Dissociable and Free Cyanide Oxidation by Ozone Injection into Gold Mine Pulp

by Coraquetzali Magdaleno López, Saúl Ortiz Landeros, Héctor Herrera Hernández, Eugenia Aldeco Pérez, Carlos Estrada Arteaga, Antonia Sandoval González and Jorge Morales Hernández

Mining 2026, 6(1), 9; https://doi.org/10.3390/mining6010009 (registering DOI) - 1 Feb 2026

Abstract

The effects of key variables on weak-acid-dissociable (WAD) and free cyanide oxidation by ozone injection in gold mine pulp were studied at laboratory scale to find an alternative cyanide treatment. A fractional factorial analysis of five process variables (O₃/O₂ flow, reaction time, NH₄HSO₃ concentration, temperature, and pH) informed a 60-run experimental matrix, in a 1 L cylindrical reactor, with the process variables controlled during the ozone injection. The findings may inform future strategies for safer cyanide management in gold mining processes. Free cyanide is the most toxic form of cyanide. Its oxidation increases with higher O₃/O₂ concentrations, longer exposure time, and higher pH. Maintaining a pH above 7 is crucial. Lower pH values favor the dissociation of cyanide into its toxic, free form. WAD cyanide oxidation depends mainly on the O₃/O₂ concentration, exposure time, and NH₄HSO₃ concentration. Increasing O₃/O₂ and time enhanced both WAD and free cyanide oxidation, while NH₄HSO₃ concentration affected oxidation rates differently. The results show that free cyanide was significantly more oxidized (84.1413%) than WAD cyanide (67.2423%). Controlling the WAD cyanide process yields excellent free cyanide oxidation. This represents ongoing improvement at an industrial scale. This approach quantifies the extent to which process variables affect the WAD and free cyanide oxidation under controlled conditions, thereby greatly reducing environmental impact. Full article

(This article belongs to the Topic Innovative Strategies to Mitigate the Impact of Mining)

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12 pages, 1290 KB

Open AccessReview

Bridging the Structural Gap: A Methodological Review of Cryo-Electron Microscopy for Underrepresented Viruses

by Yoon Ho Park, Hyun Suk Jung, Sungjin Moon and Chihong Song

Viruses 2026, 18(2), 195; https://doi.org/10.3390/v18020195 (registering DOI) - 1 Feb 2026

Abstract

Cryo-electron microscopy (cryo-EM) has revolutionized structural virology, enabling routine structure determination at 2–4 Å resolution, with exceptional cases reaching 1.56 Å. The structural diversity of viruses across vertebrate, plant, and insect hosts provides fundamental insights into infection mechanisms, host–pathogen coevolution, and therapeutic target identification. However, analysis of Electron Microscopy Data Bank entries reveals notable disparities in structural coverage: among 11,717 eukaryotic virus structures (excluding bacteriophages), vertebrate viruses constitute 97.6% (n = 11,432) of deposited entries, while plant viruses (1.0%; n = 117) and insect viruses (1.4%; n = 168) remain significantly underrepresented. This bias stems from distinct technical barriers including size limitations for giant viruses exceeding 200 nm, the loss of asymmetric information during symmetry-imposed processing, and the morphological complexity of filamentous and pleomorphic viruses. Each barrier has driven the development of specialized methodological solutions: block-based local refinement overcomes through-focus variations in giant viruses, cryo-electron tomography (cryo-ET) validates and reveals asymmetric features lost in symmetrized reconstructions, and subtomogram averaging enables structural analysis of pleomorphic assemblies. This review synthesizes recent methodological advances, critically evaluates their capacity to address specific technical barriers, and proposes strategies for expanding structural investigations across underrepresented host systems to achieve comprehensive understanding of viral structural biology. Full article

(This article belongs to the Special Issue Microscopy Methods for Virus Research)

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13 pages, 313 KB

Open AccessArticle

Root Cause Analysis of Omissions and Delays in the Initiation of Neoadjuvant Chemotherapy in Eligible Patients with Breast Cancer in British Columbia, Canada

by Jonathan L. H. Chan, Jaimie J. Lee, Hyejee Ohm, Kathryn V. Isaac and Alan Nichol

Curr. Oncol. 2026, 33(2), 87; https://doi.org/10.3390/curroncol33020087 (registering DOI) - 1 Feb 2026

Abstract

Patients with high-risk breast cancers may benefit from receiving neoadjuvant chemotherapy (NACT) to reduce tumour size and allow for breast conserving surgery. Response to NACT also informs prognosis and broadens adjuvant treatment options. According to international guidelines, NACT should commence within 28 days of diagnosis. We conducted a retrospective chart review of patients eligible for NACT at a Canadian provincial cancer centre to determine the incidence and root causes of omission or delay in initiation of NACT. Of 100 patients eligible for NACT, 73 received it, while 7 were not referred to medical oncology for consideration of NACT. Of the 73 patients who received NACT, only 15 (21%) started treatment within 28 days of diagnosis. The median diagnosis-to-NACT wait time was 40 days [IQR 30–53]. Of 54 delayed cases, 39 (72%) were due to patients waiting 21 days or more for a medical oncology consultation. Lack of, or delays in medical oncology consultation are the most prominent causes of both NACT omissions and delays. Improving triage of patients potentially eligible for NACT to medical oncology may be an effective intervention to improve patient outcomes. Full article

(This article belongs to the Section Breast Cancer)

16 pages, 1445 KB

Open AccessArticle

Life-History Plasticity of Cultured Coreius guichenoti: Energy Allocation Trade-Offs and Conservation Applications

by Miao Xiang, Haoran Liu, Zihao Meng, Yan Zhao, Chengjie Yin, Xuemei Li, Xingbing Wu and Tingbing Zhu

Animals 2026, 16(3), 456; https://doi.org/10.3390/ani16030456 (registering DOI) - 1 Feb 2026

Abstract

Coreius guichenoti, an endemic fish of the upper Yangtze River, has experienced severe population decline due to overfishing and habitat fragmentation. To inform its conservation, this study compared life-history traits between artificially bred and historical wild populations, revealing pronounced plasticity in response to environmental conditions. The cultured population, dominated by age 0–4 individuals but retaining a notable proportion of age 5–6 fish, exhibited faster growth and higher fecundity (mean absolute fecundity 32,724 ± 24,132 eggs; relative fecundity 37.5 ± 18.5 eggs/g) than the wild population. In contrast, the wild group consisted of >90% age 0–4 individuals, showed virtually no fish aged 5–7, reproduced seasonally with high total egg output, and tended toward a periodic life-history strategy, whereas the cultured group tended toward an opportunistic strategy that still retained some periodic traits. These results demonstrate that C. guichenoti can adjust its life history on a within-generation scale. Accordingly, we recommend pre-release conditioning with moderate flow and temperature variations to enhance field adaptability. This study provides evidence-based guidance for broodstock selection and preconditioning in restocking programs, aimed at improving post-release survival and reproductive success in the wild. Full article

(This article belongs to the Special Issue Changes in Population Characteristics of Marine and Freshwater Organisms and Their Environmental Driving Factors)

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13 pages, 704 KB

Open AccessArticle

Clinical Practice Guide for Integrating Diabetes-Specific Nutritional Formulas into Diabetes Care: Evidence Review and Expert Consensus

by Shanshan Lin, Gary Deed, Chee Khoo, Giuliana Murfet, Alan Winston Barclay, Glen Maberly, Anna Blackie, Wenbo Peng and Sofianos Andrikopoulos

Diabetology 2026, 7(2), 24; https://doi.org/10.3390/diabetology7020024 (registering DOI) - 1 Feb 2026

Abstract

Background/Objectives: Achieving a balanced wholefood diet while stabilising glycaemic management is challenging for many people with type 2 diabetes (T2D) due to barriers such as food preparation skills, time, and medication effects. Diabetes-specific nutritional formulas (DSNFs) are nutritionally complete products designed to support glycaemic management and overall nutritional adequacy and may complement wholefood dietary approaches when these are not feasible or are insufficient. Despite growing clinical evidence of efficacy, practical guidance for routine use is limited. Methods: A multidisciplinary expert working group developed a Clinical Practice Guide (CPG) for integrating DSNFs into diabetes care. Development was informed by a literature review and iterative consensus among experts, including representatives of the Australian Diabetes Society, Australian Diabetes Educators Association, and the Royal Australian College of General Practitioners. Results: The CPG outlines a three-step pathway: (1) assess suitability (clinical indications, contraindications, preferences, cultural context); (2) tailor the approach (individual goals, dose/timing relative to weight and body composition goals and observed glycaemic patterns, integration with lifestyle care); and (3) monitor progress (baseline, 2–4 weeks to assess initial response, then 3, 6, and 12 months for glycaemic indices, weight/body composition where available, and medication review). Conclusions: This CPG provides practical, multidisciplinary guidance for the person-centred use of DSNFs as an adjunct to standard care, supporting translation of current evidence into clinical practice and promoting consistent, multidisciplinary implementation. Full article

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13 pages, 1659 KB

Open AccessArticle

Image Feature Fusion of Hyperspectral Imaging and MRI for Automated Subtype Classification and Grading of Adult Diffuse Gliomas According to the 2021 WHO Criteria

by Ya Su, Jiazheng Sun, Rongxin Fu, Xiaoran Li, Jie Bai, Fengqi Li, Hongwei Yang, Ye Cheng and Jie Lu

Diagnostics 2026, 16(3), 458; https://doi.org/10.3390/diagnostics16030458 (registering DOI) - 1 Feb 2026

Abstract

Background: Current histopathology- and molecular-based gold standards for diagnosing adult diffuse gliomas (ADGs) have inherent limitations in reproducibility and interobserver concordance, while being time-intensive and resource-demanding. Although hyperspectral imaging (HSI)-based computer-aided pathology shows potential for automated diagnosis, it often yields suboptimal accuracy due to the lack of complementary spatial and structural tumor information. This study introduces a multimodal fusion framework integrating HSI with routinely acquired preoperative magnetic resonance imaging (MRI) to enable automated, high-precision ADG diagnosis. Methods: We developed the Hyperspectral Attention Fusion Network (HAFNet), incorporating residual learning and channel attention to jointly capture HSI patterns and MRI-derived radiomic features. The dataset comprised 1931 HSI cubes (400–1000 nm, 300 spectral bands) from histopathological patches of six major World Health Organization (WHO)-defined glioma subtypes in 30 patients, together with their routinely acquired preoperative MRI sequences. Informative wavelengths were selected using mutual information. Radiomic features were extracted with the PyRadiomics package. Model performance was assessed via stratified 5-fold cross-validation, with accuracy and area under the curve (AUC) as primary endpoints. Results: The multimodal HAFNet achieved a macro-averaged AUC of 0.9886 and a classification accuracy of 98.66%, markedly outperforming the HSI-only baseline (AUC 0.9267, accuracy 87.25%; p < 0.001), highlighting the complementary value of MRI-derived radiomic features in enhancing discrimination beyond spectral information. Conclusions: Integrating HSI biochemical and microstructural insights with MRI radiomics of morphology and context, HAFNet provides a robust, reproducible, and efficient framework for accurately predicting 2021 WHO types and grades of ADGs, demonstrating the significant added value of multimodal integration for precise glioma diagnosis. Full article

(This article belongs to the Special Issue Optical Imaging: Trends, Impact, and Application in Medical and Biomedical Diagnostics)

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