Search Results (467)

Lateral femoral neck and peritrochanteric fractures are common and clinically challenging injuries, particularly in the elderly population, with significant implications for morbidity, mortality, and functional recovery. Traditional classification systems are widely used to guide treatment, yet their reproducibility and clinical applicability remain debated. Increasing attention has been directed toward trabecular architecture and its role in fracture behavior and reduction strategies. This review aims to summarize current evidence on classification systems, trabecular-based fracture patterns, pre-operative reduction techniques, and fixation strategies. A narrative review was conducted using PubMed/MEDLINE, Embase, and Scopus databases up to May 2026. Original studies, reviews, and biomechanical investigations focusing on proximal femur fracture classification, reliability, trabecular alignment, reduction techniques, and fixation methods were included. Data were qualitatively analyzed, with emphasis on interobserver reliability, biomechanical implications, and clinical outcomes. Conventional classification systems, including anatomical, Evans–Jensen, and AO/OTA frameworks, demonstrated variable and generally moderate reproducibility, with reported interobserver agreement ranging from approximately κ = 0.30 to 0.60. Emerging evidence highlights the importance of trabecular architecture, distinguishing intradigital fractures—confined within trabecular pathways and relatively stable—from extradigital fractures, which disrupt load-bearing structures and are associated with increased mechanical instability and higher failure rates. Biomechanical and clinical studies indicate that inadequate reduction with trabecular misalignment significantly increases the risk of varus collapse and implant cut-out. Reduction strategies tailored to fracture pattern, such as internal rotation for intradigital fractures and external or combined maneuvers for extradigital patterns, improve alignment and load transfer. In terms of fixation, dynamic hip screws remain effective in stable fractures, whereas cephalomedullary nails demonstrate superior performance in unstable patterns, with lower reoperation rates reported (approximately 5–8% vs. 10–15%). Management of lateral femoral neck and peritrochanteric fractures should extend beyond traditional classification systems to incorporate trabecular biomechanics. Restoration of trabecular alignment, alongside established parameters such as neck–shaft angle and tip–apex distance, is critical for optimizing outcomes. Further prospective studies are needed to validate trabecular-based classifications and standardize reduction strategies. Full article

Background: Respiratory diseases (RDs), including asthma, COVID-19, chronic obstructive pulmonary disease (COPD), and pneumonia, remain a major global health challenge, contributing substantially to global morbidity and mortality. Conventional diagnosis relies heavily on clinicians’ expertise to interpret respiratory sounds and radiographic images, a process that can be subjective, time-consuming, and prone to inter-observer variability. Recent advances in artificial intelligence (AI) and machine learning (ML) have enabled automated diagnostic approaches that can improve the efficiency, consistency, and scalability of respiratory disease detection. However, existing research remains fragmented across different data modalities. Methods: This review systematically analyzes recent studies on AI-based respiratory disease detection using both visual modalities (e.g., chest X-rays, computed tomography (CT) scans, and ultrasound) and audio modalities (e.g., cough and breath sounds). To provide a comprehensive perspective, the reviewed literature is organized using a unified taxonomy that categorizes existing approaches into three main groups: audio-based, visual-based, and audio–visual-based methods. In addition, two conceptual frameworks are proposed to illustrate representative pipelines for audio-based and visual-based respiratory disease classification. Results: The analysis reveals that most existing studies focus on single-modality approaches, while multimodal integration remains relatively underexplored. Only a limited number of studies combine audio and visual data within unified frameworks, primarily due to the scarcity of synchronized multimodal datasets collected from the same patients. The proposed taxonomy and conceptual frameworks provide a structured basis for comparing existing methods, identifying methodological trends, and highlighting key research gaps in multimodal respiratory disease detection. Conclusions: Future research should prioritize the development of multimodal datasets, robust evaluation protocols, and interpretable and lightweight AI models suitable for real-world clinical deployment. Advancing multimodal integration has the potential to significantly enhance the accuracy, reliability, and clinical applicability of AI-driven respiratory disease diagnosis systems. Full article

Accurate assessment of vitreous inflammation is essential for the diagnosis, monitoring and management of uveitis. Traditionally, vitritis has been evaluated using subjective clinical grading systems based on vitreous haze and cellular infiltration, which are limited by interobserver variability and poor reproducibility, particularly in cases of mild or subclinical inflammation. In recent years, advances in ocular imaging have enabled the development of more objective, quantitative approaches. Ultra-widefield imaging, optical coherence tomography (OCT) and ultrasound-based techniques have provided new insights into structural alterations within the vitreous. In parallel, automated image analysis and artificial intelligence (AI)-based methods have improved the detection and quantification of inflammatory biomarkers, including vitreous hyperreflective foci and signal intensity-based metrics. Despite these advances, important limitations remain, including a restricted field of view, a lack of standardized segmentation algorithms and an incomplete representation of the entire vitreous cavity. No single modality currently provides a comprehensive and fully reproducible assessment of vitreous inflammation. This review summarizes current qualitative and quantitative methods for evaluating vitreous inflammation, highlighting their respective strengths and limitations. In addition, emerging diagnostic strategies, including multimodal imaging integration, AI-driven analysis and molecular biomarker profiling, are discussed as potential tools to improve accuracy, standardization and clinical applicability. The transition from subjective grading toward objective quantification of inflammatory burden represents a key step in advancing both clinical management and research in ocular inflammatory diseases. Full article

Background: Deep neural network based prostate segmentation depends on manual annotations, yet the effect of annotation variability on model performance remains underexplored. Methods: Prostate contours were manually delineated by an expert clinician on 119 T2-weighted MR images from the PROSTATEx Challenge 2017 training dataset, and slice-wise synthetic radial modifications of 1–10 mm were applied to create 10 modified training datasets plus an unmodified baseline. Identical SegResNet models were trained with Auto3DSeg/MONAI and evaluated against unmodified validation and test sets using the Dice similarity coefficient (DSC). Results: Mean test DSC decreased from 0.917 for the baseline model to 0.856 at 10 mm modification. Models trained with small annotation perturbations of 1–5 mm maintained DSC values of at least 0.90, whereas performance declined significantly beyond 5 mm. Pairwise DSC agreement across modified annotations also fell as modification amplitude increased. Conclusions: Prostate segmentation models tolerated modest annotation variability but degraded substantially when variability exceeded 5 mm, underscoring the importance of annotation quality when training and benchmarking DNN-based automated segmentation models. Full article

Background/Objectives: Alignment philosophy in total knee arthroplasty (TKA) may affect joints beyond the knee. Mechanical alignment (MA) targets a neutral mechanical axis, whereas kinematic alignment (KA) aims to restore native alignment and joint line obliquity (JLO). This study compares the effects of MA and KA on hip and ankle radiographic parameters and investigates the propagation of coronal correction along the lower limb. Methods: A retrospective comparative study evaluated 63 TKAs performed for varus deformity (KA: n = 32; MA: n = 31). Pre- and postoperative full-length standing radiographs were used to calculate changes (Δ), defined as the difference between postoperative and preoperative values, in hip offsets, mechanical and arithmetic hip–knee–ankle angles (mHKA, aHKA), medial proximal tibial angle (MPTA), lateral distal femoral angle (LDFA), JLO, and ankle ground-referenced angles. Between-group differences and correlations were analysed. Interobserver reliability was assessed for all variables. Results: MA produced significantly greater limb correction than KA (ΔmHKA: 8.89° vs. 4.82°, p < 0.001), primarily due to increased tibial valgus correction (ΔMPTA: 6.26° vs. 2.41°, p < 0.001). JLO increased substantially with MA (+4.10°) but was preserved with KA (+0.30°, p < 0.001). MA resulted in significant valgus shifts at the ankle (ground talar dome angle (GTDA) −3.01°, ground tibial plafond angle (GTPA) −3.02°; p = 0.006 for both), whereas KA produced no significant ankle changes. Correlation analysis demonstrated limited knee–ankle biomechanical coupling, with a moderate negative correlation in MA (ΔmHKA vs. ΔGTDA: ρ = −0.479, p = 0.006) and a weak correlation in KA (ΔaHKA vs. ΔGTDA: ρ = −0.360, p = 0.043). Hip parameters remained unchanged in both groups. Conclusions: Mechanical alignment induces larger tibial-driven coronal corrections, increases joint line obliquity, and produces measurable valgus shift at the ankle. In contrast, kinematic alignment preserves native alignment and joint-line obliquity while minimising distal ankle compensatory changes. Full article

Background: Allergic contact dermatitis (ACD) is a common inflammatory skin disease and patch testing (PT) remains the gold standard for its diagnosis; however, PT interpretation is time-consuming and prone to inter-observer variability. Growing advances in digital imaging and artificial intelligence (AI) have encouraged the development of automated PT evaluation systems. This review aimed to summarize the use of deep learning networks (DNNs) and image-preprocessing techniques for PT classification. Methods: A literature review was conducted to identify original research published between 2020 and 2025 that applied deep learning algorithms to PT image analysis. Included studies were assessed with respect to model architecture, dataset characteristics, preprocessing strategies, and diagnostic performance. Results: Six original studies employing deep learning for PT image classification met the inclusion criteria. They employed a range of architectures, including YOLOv5x, EfficientNetB0, Xception, and custom CNN models. Reported diagnostic performance varied, with accuracy values ranging from 90% to 99.5%, F1-scores from 0.37 to 0.98, and AUROC values up to 0.94. Despite promising results, models remain unreliable for ICDRG grading, especially for severe reactions, and methodological variability in dataset composition, imaging conditions, preprocessing pipelines, and classification tasks limits comparability across studies. Conclusions: Deep learning shows promise for automated PT interpretation, but further standardized and multicenter studies with detailed preprocessing protocols and comprehensive ICDRG grading are required for clinical implementation. Full article

Background/Objectives: Although the Spinal Instability Neoplastic Score (SINS) is widely used to estimate spinal metastases fracture risk and guide decisions on stabilisation procedures, prior studies have demonstrated mixed results. Patients with the same score exhibit clinically heterogeneous outcomes, with some SINS criteria correlating less well with the estimated fracture risk than others. There are also barriers to implementation such as the time burden required for manual calculation and interobserver variability associated with qualitative morphological criteria. SINS also lacks sensitivity for detecting latent structural compromise in treatment-naive patients and those susceptible to the iatrogenic effects of stereotactic body radiation therapy. This review aims to evaluate emerging imaging, biomechanical, and microstructural markers with the potential to improve fracture risk stratification and prognostication for spinal oncology patients. Methods: We synthesise evidence across three innovative frontiers: (1) biomechanical modelling, including CT-derived finite element analysis and failure-load pattern models; (2) radiomics, utilizing radiomics features from radiological imaging to develop a predictive model; and (3) microstructural MRI biomarkers, exploring the translatability of the Vertebral Bone Quality score, fat fraction, and paraspinal muscle atrophy from osteoporosis to the metastatic spine. Results: Emerging biomechanical, radiomic and microstructural imaging markers show potential in addressing some limitations of traditional SINS criteria for fracture risk stratification across the spinal oncology treatment continuum, from initial diagnosis to post-radiation surveillance, thereby facilitating more precise risk assessment. However, current evidence remains largely retrospective and heterogeneous, and further validation is required before clinical adoption. Conclusions: We propose a framework that shifts the paradigm from conventional morphological scoring toward a multiparametric assessment of spinal stability. Full article

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a significant and escalating global health concern, with an estimated prevalence of 30%. Current assessments of hepatic steatosis, a hallmark of MASLD, rely on semi-quantitative grading by pathologists, which is inherently limited by inter-observer variability. Objective: To address this limitation, we developed a novel deep learning pipeline, named SteatoStat, to standardize and enhance the quantification of hepatic steatosis in patients with MASLD. Method: The SteatoStat pipeline employs and integrates multiple components such as file format standardization, rule-based cell filtering, and multiple segmentation models across various liver structures, resulting in an output of a continuous quantitative measure of steatosis percentage and translated into steatosis grades. Results: We report a high degree of accuracy and reliability with SteatoStat achieving the following performance metrics (DICE score = 0.8955, AUROC = 0.9928, F1 score = 0.8990). When benchmarked against expert pathologists, the weighted Kappa coefficient is 0.837. Furthermore, in comparison with an existing, well-established model, SteatoStat demonstrated a weighted Kappa coefficient = 0.765. Conclusions: These robust findings underscore its potential clinical utility in providing a standardized objective quantification of hepatic steatosis. Future directions include enhancing the model’s generalizability and its clinical integration through validation on independent, multi-institutional datasets. Full article

Manual classification of breast cancer is resource-intensive, slow, and subject to inter-observer variability, motivating automated deep learning solutions. Most current methods rely on unimodal imaging data and struggle with domain generalization (DG) across varied clinical environments. We propose a Dual-Stage Multimodal Alignment approach that integrates breast ultrasound (US) imagery with clinical text reports to improve diagnostic stability. The method proceeds in two stages: (1) Local Correlation Alignment (LCA), which aligns fine-grained visual features with textual embeddings to capture localized lesion attributes, and (2) Global Attention Alignment (GAA), which applies multi-head self-attention to the joint visual–textual sequence to encourage domain-invariant representations. We evaluate the approach on a harmonized, leakage-free repository of 6880 images aggregated from six public US datasets (BUS-CoT, BrEaST, BUS-BRA, BUS-UCLM, BLUI, BUSI) under three protocols: independent benchmarking on BUS-CoT, pooled cross-dataset evaluation, and zero-shot domain generalization on unseen unimodal target domains. On the BUS-CoT benchmark, the 198M-parameter model reaches 0.8177 accuracy and 0.8852 AUC, on par with the 7-billion-parameter Qwen2.5-VL-7B with chain-of-thought reasoning (0.8064 accuracy, 0.8354 AUC) while using roughly 1/35 the parameter count. In the pooled setting, it is competitive with single-domain state-of-the-art methods on individual subsets (e.g., 0.9576 AUC on BUSI, 0.8741 accuracy on BUS-BRA). Under zero-shot transfer without clinical text, per-domain AUC ranges from 0.7360 to 0.8060 across four unseen targets, providing a lower bound under cross-scanner shift. These results indicate that task-specific multimodal alignment can rival large vision-language models in breast US diagnosis at a fraction of the parameter count. Full article

Background/Objectives: Differentiating mediastinal lymphadenopathy associated with lung cancer from sarcoidosis remains a clinical challenge because of overlapping imaging findings. This study evaluated whether CT-derived radiomic features, alone and in combination with clinical variables, could support the non-invasive differentiation of these two entities. Methods: In this retrospective single-center study, 204 histopathologically confirmed mediastinal lymph nodes were analyzed. A total of 107 radiomic features were extracted from manually segmented contrast-enhanced thoracic CT images. Multiple feature selection methods, dimensionality reduction techniques, and machine learning classifiers were evaluated using patient-level five-fold cross-validation. Additional clinical-only, combined clinical–radiomic, one-node-per-patient sensitivity, and exploratory interobserver feature stability analyses were performed. Results: Among radiomics-only models, LASSO achieved the highest ROC–AUC of 0.9108, whereas ElasticNet achieved the highest accuracy of 81.20%. The clinical-only ensemble model using age, sex, and smoking status showed strong performance, with an accuracy of 94.92% and an ROC–AUC of 0.9733. Selected combined clinical–radiomic models showed numerically higher performance; the combined correlation-filtered ensemble model achieved the highest accuracy of 97.78% and an ROC–AUC of 1.0000. Clinical integration also yielded more compact feature subsets in some methods, as combined LASSO selected 9.6 variables while improving ROC–AUC from 0.9108 to 0.9667 compared with radiomics-only LASSO. In the one-node-per-patient sensitivity analysis, clinical-only and combined models retained high performance, whereas radiomics-only LASSO showed reduced performance. Exploratory interobserver analysis showed moderate reproducibility for only a subset of radiomic features. Conclusions: CT-derived radiomic features may provide complementary information for differentiating mediastinal lymphadenopathy associated with lung cancer from that associated with sarcoidosis. However, clinical variables were highly informative, and the incremental value of radiomics should be interpreted cautiously. Further multicenter studies with external validation, standardized segmentation protocols, and clinically balanced cohorts are required before routine clinical implementation can be recommended. Full article

Acute hypoxemic respiratory failure (AHRF) represents one of the most common and clinically challenging indications for invasive mechanical ventilation in the intensive care unit, characterized by profound etiological heterogeneity that demands accurate diagnosis to guide treatment. While clinical history, physical examination, and laboratory data remain essential, they are often insufficient to reliably discriminate among conditions such as acute respiratory distress syndrome (ARDS), cardiogenic pulmonary edema, and pneumonia—particularly in mechanically ventilated patients. Lung imaging has therefore emerged as an indispensable complement to clinical assessment. In this narrative review, we systematically describe the physical principles, clinical applications, and limitations of the imaging modalities currently available in critical care: chest X-ray (CXR), computed tomography (CT), lung ultrasound (LUS), electrical impedance tomography (EIT), and positron emission tomography (PET). CXR remains the most widely used bedside tool but is constrained by low sensitivity and significant interobserver variability. CT is the gold standard for morphological and quantitative lung phenotyping, enabling the assessment of recruitability, baby lung characterization, and the identification of complications, but requires patient transport and exposes patients to ionizing radiation. LUS offers real-time, bedside evaluation of aeration with high diagnostic accuracy for pneumothorax and pleural effusion, and is increasingly integrated into revised ARDS diagnostic criteria. EIT enables continuous, radiation-free monitoring of regional ventilation distribution and positive end-expiratory pressure (PEEP)-guided titration directly at the bedside. While PET provides unparalleled quantification of regional inflammation and ventilation-perfusion mismatch, it currently remains a purely investigative research tool. Finally, we discuss emerging technological and AI-driven advances—including dual-energy CT, next-generation EIT, and deep learning algorithms—that are poised to transform lung imaging from a passive diagnostic tool into an active, personalized guide to respiratory management. Full article

Cerebrovascular diseases, including stroke and intracranial aneurysm, affect millions worldwide and remain a leading cause of mortality and disability. Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) enables non-invasive visualization of intracranial arteries. However, the complex cerebrovascular anatomy, characterized by variable diameters, tortuous trajectories, and intricate branching, renders manual segmentation time-consuming, subjective, and prone to inter-observer variability. While deep learning models achieve strong segmentation performance, existing 3D approaches typically require millions of parameters, limiting deployment in resource-constrained clinical settings. To address this challenge, this paper proposes a Lightweight Intracranial Vascular Segmentation Network (LIVAS-Net), a parameter-efficient 3D encoder–decoder architecture using 3D Ghost convolution modules. It incorporates a novel Vessel Continuity Refinement Branch (VCRB), which aims to correct discontinuities in logit space through per-voxel learnable gating. Two model variants are introduced, LIVAS-Net (129K parameters, 18.3 GFLOPs) and LIVAS-L Net (2.97M parameters, 87.8 GFLOPs), achieving 7.9× and 1.6× fewer FLOPs than the standard 3D U-Net (144.5 GFLOPs), respectively. Evaluation on the multi-center COSTA benchmark shows a DSC of 0.8943 (HD95: 1.97 mm) and 0.9235 (HD95: 0.77 mm) on the ADAM test set, outperforming 3D U-Net (DSC: 0.8762). Cross-center evaluation on three external COSTA datasets yields overall DSCs of 0.7834 and 0.7967 versus 0.6998 for 3D UNet. Further evaluation on the CereVessMRA dataset (N = 271) reveals that LIVAS-Net achieves the highest DSC (0.669), demonstrating promising experimental results warranting future clinical validation in resource-constrained settings. Full article

Background: Platelet-rich plasma (PRP) is utilised in the treatment of patellofemoral chondropathy, although clinical responses remain variable. This retrospective exploratory study assessed whether baseline quantitative T2* mapping of femoral cartilage was associated with clinical improvement following PRP administration. Methods: In this retrospective observational study conducted within routine clinical practice, patients with patellofemoral chondropathy received three ultrasound-guided intra-articular PRP injections administered weekly according to an institutional protocol. Baseline and 9-month T2*-mapping MRI scans and clinical questionnaires were collected as part of standard follow-up. The main imaging variable was the worst-region femoral trochlear T2* value, evaluated as a candidate prognostic biomarker. Clinical outcomes included the Visual Analogue Scale (VAS, 0–10) and Kujala (0–100) scores, with responders defined by minimum clinically important difference (MCID) thresholds (ΔVAS ≥ 1.5; ΔKujala ≥ 8). Results: Thirty-two knees from 22 patients completed follow-up, including 10 bilateral cases (19 right knees, 13 left knees). Both VAS and Kujala scores improved significantly at 9 months (p < 0.001 for both). Baseline femoral trochlear worst-region T2* values were inversely correlated with pain and functional improvement (ΔVAS: rho = −0.51, p = 0.003; ΔKujala: rho = −0.36, p = 0.042). Baseline patellar T2* values were not associated with clinical change (ΔVAS: rho = −0.18, p = 0.32; ΔKujala: rho = −0.12, p = 0.51). Sensitivity analyses using baseline mean femoral T2* values did not show significant associations with ΔVAS or ΔKujala. Interobserver reproducibility for the worst-region T2* metric was limited, particularly for the femoral compartment (femur ICC 0.37; patella ICC 0.47), which limits immediate clinical applicability. Mean regional longitudinal ΔT2* changes did not exceed the 14% QIBA MDC95 threshold. Conclusions: In this small retrospective cohort, baseline femoral trochlear worst-region T2* values were associated with clinical improvement after PRP. These preliminary hypothesis-generating findings should be interpreted with caution and require validation in larger controlled cohorts with standardised and reproducible segmentation workflows. Full article

Clinical evaluation of scoliosis primarily relies on the Cobb angle measured on standing whole-spine radiographs. However, manual measurement is affected by intra- and inter-observer variability caused by differences in end-vertebra selection, endplate definition, and vertebral boundary interpretation. In addition, low radiographic contrast and anatomical overlap can hinder accurate identification of the spinal contour. In clinical screening, rapid three-level severity classification with reduced false negatives serves as a complementary function to precise quantitative measurement, supporting case triage and missed-detection prevention. This study proposes a boundary-enhanced YOLO-based instance segmentation framework—where ‘boundary-enhanced’ refers to the reinforcement of spinal contour boundary representation through the DeepLabV3+-based segmentation head—for three-level scoliosis severity screening using clinician-assigned severity labels derived from Cobb angle measurements. Unlike semantic segmentation, which may cause class fragmentation within a single spine, the proposed method defines the entire spine as one anatomical instance and predicts a single severity label based on the global contour structure. Class-balanced offline augmentation, background-only negative samples, attention modules, and segmentation heads were comparatively evaluated. Results showed that background-only negative samples reduced false negatives, and CBAM improved accuracy while maintaining a practical model size and near-real-time inference speed under the tested environment. DeepLabV3+ provided the most stable contour reconstruction. The final model improved both contour extraction and three-level severity screening performance, suggesting that the proposed framework may be potentially useful for assisting scoliosis screening. However, further external validation and prospective evaluation are required before clinical deployment. Full article

Background/Objectives: Celiac disease (CD) is a T-cell-mediated autoimmune condition, triggered by gluten ingestion. Duodenal biopsy is the gold-standard diagnosis for CD, which is often limited by interobserver variability between pathologists. Immunohistochemistry (IHC) is a powerful technique for detecting biomarkers with potential diagnostic significance. This study aims to investigate five candidate biomarkers, BTNL8, NKp46, TdT, THEMIS, and TCRδ, that might improve the reproducibility of the diagnosis of CD. Methods: Formalin-fixed paraffin-embedded material, surplus to diagnostic requirements, was obtained from 46 subjects (untreated CD: n = 21, CD treated with gluten-free diet: n = 5; controls: n = 20) and immunostained for BTNL8, NKp46, TdT, THEMIS and TCRδ. BTNL8 staining was scored on a 0–3 semi-quantitative scale. NKp46, TdT, THEMIS, and TCR delta-positive intra-epithelial lymphocytes (IELs) were quantified as mean counts per 100 epithelial cells (ECs). Results: TCRδ-positive IELs were markedly elevated in CD biopsies (median 9.4 IELs/100 ECs) compared to healthy controls (median 0.5 IELs/100 ECs; p < 0.001), with a threshold of >2.1 TCRδ-positive IELs per 100 ECs yielding an AUC of 0.94 and interobserver agreement of 0.82. NKp46 expression was also increased in CD (median 13.8 IELs/100 ECs) versus controls (median 9.6; p < 0.001), with >12.8 NKp46-positive IELs per 100 ECs achieving an AUC of 0.86 and interobserver agreement of 0.82. Immunostaining for the other biomarkers demonstrated less clear differences between CD and healthy controls. Conclusions: Corroborating several recent publications, TCRδ immunostaining provides high diagnostic accuracy and good interobserver agreement in the diagnosis of CD on duodenal biopsy, even for patients on a gluten-free diet. Full article