Search Results (4,525)

Background: Vascular calcification has been highlighted as a prognostic factor for perioperative thrombosis but a protective factor for late restenosis in lower limb peripheral artery disease (LLPAD). The aim of this study was to investigate the association between calcification and twelve-month primary patency in patients with stenting of the common femoral artery (CFA) and its bifurcation for atheromatous stenosis. Materials/Methods: This single-center retrospective study analyzed consecutive limbs (n = 90) that underwent CFA stenting for symptomatic lesions between January 2018 and January 2023. Calcification was assessed using dedicated computed tomography angiography analysis software (EndoSize; Therenva), with blinded evaluation of volume (mm³) and density (Hounsfield Units) across three anatomically distinct zones: proximal CFA (Zone 1); distal CFA (Zone 2); and bifurcation segments (Zone 3). The primary endpoint was twelve-month primary patency, defined as a peak systolic velocity ratio (PSVR) < 2.4 on duplex ultrasound without target lesion revascularization. Secondary endpoints included predictors of restenosis using multivariable logistic regression. Results: Ninety cases of CFA stenting for LLPAD (lower limb peripheral artery disease) were analyzed. A total of 78.9% of CFA lesions were treated for claudication and 21.1% for critical limb-threatening ischemia (CLTI). Lesions were distributed as Azema types I (1%), II (43%), and III (56%). At twelve-month follow-up, primary patency (PSVR < 2.4) was achieved in 77.4% of limbs. Patent CFA stenting demonstrated significantly higher median calcification density in Zone 2 compared to those with restenosis (1122 [IQR: 903–1248] vs. 858 [788–987] HU; p = 0.006; q = 0.021 after false discovery rate correction). ROC curve analysis identified a density threshold of 800 HU with a 76% reduction in restenosis risk (OR 0.24; 95% CI: 0.08–0.72; p = 0.011). Bootstrap validation (1000 replications) confirmed threshold stability at 821 HU (95% CI: 656–990 HU). Conclusions: In this exploratory study, dense calcification (≥800 HU) in the distal CFA appears to be protective against twelve-month restenosis following stenting. These findings suggest that calcification density may serve as a valuable predictor for patient selection and procedural planning in CFA interventions. Full article

Introduction: Primary glomerulopathies are immune-driven kidney diseases. IgA nephropathy (IgAN) and membranoproliferative glomerulonephritis (MPGN) are prevalent entities with a risk of chronic progression. Immune checkpoints, such as PD-1/PD-L1, CTLA-4/CD86, and CD200R/CD200, regulate activation and tolerance in T, B, and NK cells, and also exist in soluble forms, reflecting systemic immune balance. Objective: To compare immune checkpoint profiles in IgAN and MPGN versus healthy volunteers (HV) through surface expression, soluble serum levels, and PBMC transcripts, with attention to sex-related differences and diagnostic value assessed by ROC curves. Materials and Methods: Ninety age-matched subjects were studied: IgAN (n = 30), MPGN (n = 30), HV (n = 30). Flow cytometry evaluated checkpoint expression on CD4+/CD8+ T cells, CD19+ B cells, and NK cells. ELISA quantified sPD-1, sPD-L1, sCTLA-4, sCD86, sCD200, sCD200R; PBMC transcript levels were assessed. Group comparisons, sex stratification, and ROC analyses were performed. Results: Lymphocyte distributions were preserved, but IgAN patients showed anemia and impaired renal function, while MPGN patients had greater proteinuria and dyslipidemia. GN patients displayed increased PD-1/PD-L1 and CD200R/CD200, with reduced CTLA-4/CD86, compared to HV. Serum analysis revealed elevated sPD-1, sPD-L1, sCD200, sCD200R and decreased sCTLA-4, sCD86. PBMC transcripts paralleled these trends, with PD-1/PD-L1 mainly increased in MPGN. Sex had minimal impact. ROC analyses showed strong GN vs. HV discrimination by CD19⁺CTLA-4⁺, PD-1/PD-L1, and CD200/CD200R, but limited ability to separate IgAN from MPGN. Conclusions: IgAN and MPGN share a sex-independent checkpoint signature: PD-1/PD-L1 and CD200R/CD200 upregulation with CTLA-4/CD86 downregulation. CD19⁺, CTLA-4⁺, and soluble PD-1/PD-L1/CD200(R) emerge as promising biomarkers requiring further validation. Full article

Background: Lumbar disk herniation is a common cause of low back pain and radiculopathy, significantly impacting patients’ life quality and functional capacity. Magnetic Resonance Imaging (MRI) remains the gold standard for its assessment due to its superior soft tissue contrast and multiplanar imaging capabilities. However, recent advances in spectral computed tomography (CT), particularly dual-energy CT (DECT), have introduced new diagnostic opportunities, offering improved soft tissue characterization. Objective: To evaluate the diagnostic performance of DECT in detecting and grading lumbar disk herniations using dedicated color-coded fat maps. Materials and Methods: A total of 205 intervertebral levels from 41 consecutive patients with lumbar symptoms were prospectively analyzed. All patients underwent both DECT and MRI within 3 days. Three radiologists with varying years of experience independently assessed DECT images using color-coded reconstructions. A five-point grading score was attributed to each lumbar level: 1 = normal disk, 2 = bulging/protrusion, 3 = focal herniation, 4 = extruded herniation, and 5 = migrated fragment. The statistical analysis included Pearson’s correlation for score consistency, Cohen’s Kappa for interobserver agreement, generalized estimating equations for a cluster-robust analysis, and an ROC curve analysis. The DECT diagnostic accuracy was assessed in a dichotomized model (grades 1–2 = no herniation; 3–5 = herniation), using MRI as reference. Results: A strong correlation was observed between DECT and MRI scores across all readers (mean Pearson’s r = 0.826, p < 0.001). The average exact agreement between DECT and MRI was 79.4%, with the highest concordance at L1–L2 (86.7%) and L5–S1 (80.4%). The interobserver agreement was substantial (mean Cohen’s κ = 0.765), with a near-perfect agreement between the two most experienced readers (κ = 0.822). The intraclass correlation coefficient was 0.906 (95% CI: 0.893–0.918). The ROC analysis showed excellent performance (AUC range: 0.953–0.986). In the dichotomous model, DECT demonstrated a markedly higher sensitivity than conventional CT (95.1% vs. 57.2%), with a comparable specificity (DECT: 99.0%; CT: 96.5%) and improved overall accuracy (98.4% vs. 90.0%). Subgroup analyses by age and disk location revealed no statistically significant differences. Conclusions: The use of DECT dedicated color-coded fat map reconstructions showed high diagnostic performance in the assessment of lumbar disk herniations compared to MRI. These findings support the development of dedicated post-processing tools, facilitating the broader clinical adoption of spectral CT, especially in cases where MRI is contraindicated or less accessible. Full article

Background/Objectives: Central precocious puberty (CPP) is diagnosed via gonadotropin-releasing hormone (GnRH) stimulation testing, which can be burdensome in pediatric settings. This study evaluated the utility of baseline hormonal markers—particularly insulin-like growth fac-tor 1 (IGF-1) and IGF-binding protein 3 (IGFBP-3)—as auxiliary tools for CPP diagnosis in Korean children. Methods: We retrospectively analyzed patients who underwent GnRH stimulation testing. Baseline LH, FSH, IGF-1, and IGFBP-3 levels were assessed, along with standard deviation scores (SDS) calculated using two different reference intervals. Multivariable logistic regression was performed to improve diagnostic accuracy. Performance was evaluated using area under the curve (AUC) values from receiver operating characteristic (ROC) analyses, stratified by sex. Results: Among 2464 Korean children (2025 girls and 439 boys), CPP diagnosis rates were 54.2% in girls and 65.6% in boys. Among baseline markers, FSH showed the highest AUCs using raw values with sex-specific cutoffs (AUC = 0.767 in girls and 0.895 in boys). Although IGF-1 SDS and IGFBP-3 SDS showed AUCs < 0.7 when used alone, predictive models incorporating these SDS values yielded higher performance (AUC = 0.800 in girls and 0.920 in boys. Conclusions: SDS-based IGF-1 and IGFBP-3 enhance CPP diagnosis when used in predictive models, emphasizing the need for sex-specific interpretation and standardized reference intervals in real-world clinical practice. Full article

A critical factor contributing to the burden of childhood asthma is the lack of effective self-management in homecare settings. Artificial intelligence (AI) and lung sound monitoring could help address this gap. Yet, existing AI-driven auscultation tools focus on wheeze detection and often rely on subjective human labels. To improve the early detection of asthma worsening in children in homecare setting, we trained and evaluated a Deep Learning model based on spirometry-labelled lung sounds recordings to detect asthma exacerbation. A single-center prospective observational study was conducted between November 2020 and September 2022 at a tertiary pediatric pulmonology department. Electronic stethoscopes were used to record lung sounds before and after bronchodilator administration in outpatients. In the same session, children also underwent spirometry, which served as the reference standard for labelling the lung sound data. Model performance was assessed on an internal validation set using receiver operating characteristic (ROC) curves. A total of 16.8 h of lung sound recordings from 151 asthmatic pediatric outpatients were collected. The model showed promising discrimination performance, achieving an AUROC of 0.763 in the training set, but performance in the validation set was limited (AUROC = 0.398). This negative result demonstrates that acoustic features alone may not provide sufficient diagnostic information for the early detection of asthma attacks, especially in mostly asymptomatic outpatients typical of homecare settings. It also underlines the challenges introduced by differences in how digital stethoscopes process sounds and highlights the need to define the severity threshold at which acoustic monitoring becomes informative, and clinically relevant for home management. Full article

Cardiovascular diseases (CVDs) remain the leading cause of global mortality, with ischemic heart disease (IHD) being the most prevalent and deadly subtype. The growing burden of IHD underscores the urgent need for effective early detection methods that are scalable and non-invasive. Heart Rate Variability (HRV), a non-invasive physiological marker influenced by the autonomic nervous system (ANS), has shown clinical relevance in predicting adverse cardiac events. This study presents a photoplethysmography (PPG)-based Zhurek IoT device, a custom-developed Internet of Things (IoT) device for non-invasive HRV monitoring. The platform’s effectiveness was evaluated using HRV metrics from electrocardiography (ECG) and PPG signals, with machine learning (ML) models applied to the task of early IHD risk detection. ML classifiers were trained on HRV features, and the Random Forest (RF) model achieved the highest classification accuracy of 90.82%, precision of 92.11%, and recall of 91.00% when tested on real data. The model demonstrated excellent discriminative ability with an area under the ROC curve (AUC) of 0.98, reaching a sensitivity of 88% and specificity of 100% at its optimal threshold. The preliminary results suggest that data collected with the “Zhurek” IoT devices are promising for the further development of ML models for IHD risk detection. This study aimed to address the limitations of previous work, such as small datasets and a lack of validation, by utilizing real and synthetically augmented data (conditional tabular GAN (CTGAN)), as well as multi-sensor input (ECG and PPG). The findings of this pilot study can serve as a starting point for developing scalable, remote, and cost-effective screening systems. The further integration of wearable devices and intelligent algorithms is a promising direction for improving routine monitoring and advancing preventative cardiology. Full article

Background: Early detection of dysglycemia in young adults is important but underexplored. This study aimed to (1) predict long-term changes in fasting plasma glucose (δ-FPG) and (2) classify future prediabetes using complementary machine learning (ML) approaches. Methods: We analyzed 6247 Taiwanese men aged 18–35 years (mean follow-up 5.9 years). For δ-FPG (continuous outcome), random forest, stochastic gradient boosting (SGB), eXtreme gradient boosting (XGBoost), and elastic net were compared with multiple linear regression using Symmetric mean absolute percentage error (SMAPE), Root mean squared error (RMSE), Relative absolute error(RAE), and Root relative squared error (RRSE) Sensitivity analyses excluded baseline FPG (FPG_base). Shapley additive explanations(SHAP) values provided interpretability, and stability was assessed across 10 repeated train–test cycles with confidence intervals. For prediabetes (binary outcome), an XGBoost classifier was trained on top predictors, with class imbalance corrected by SMOTE-Tomek. Calibration and decision-curve analysis (DCA) were also performed. Results: ML models consistently outperformed regression on all error metrics. FPG_base was the dominant predictor in full models (100% importance). Without FPG_base, key predictors included body fat, white blood cell count, age, thyroid-stimulating hormone, triglycerides, and low-density lipoprotein cholesterol. The prediabetes classifier achieved accuracy 0.788, precision 0.791, sensitivity 0.995, ROC-AUC 0.667, and PR-AUC 0.873. At a high-sensitivity threshold (0.2892), sensitivity reached 99.53% (specificity 47.46%); at a balanced threshold (0.5683), sensitivity was 88.69% and specificity was 90.61%. Calibration was acceptable (Brier 0.1754), and DCA indicated clinical utility. Conclusions: FPG_base is the strongest predictor of glycemic change, but adiposity, inflammation, thyroid status, and lipids remain informative. A dual interpretable ML framework offers clinically actionable tools for screening and risk stratification in young men. Full article

Arthritis and arthrosis are prevalent joint diseases that cause pain and disability, and their early diagnosis is crucial for preventing irreversible damage. Conventional diagnostic methods such as X-ray, ultrasound, and MRI have limitations in early detection, prompting interest in alternative techniques. This work presents the design and clinical evaluation of a prototype device for non-invasive early diagnosis of arthritis (inflammatory joint disease) and arthrosis (osteoarthritis) using infrared thermography and deep neural networks. The portable prototype integrates a Raspberry Pi 4 microcomputer, an infrared thermal camera, and a touchscreen interface, all housed in a 3D-printed PLA enclosure. A custom Flask-based application enables two operational modes: (1) thermal image acquisition for training data collection, and (2) automated diagnosis using a pre-trained ResNet50 deep learning model. A clinical study was conducted at a university clinic in a temperature-controlled environment with 100 subjects (70% with arthritic conditions and 30% healthy). Thermal images of both hands (four images per hand) were captured for each participant, and all patients provided informed consent. The ResNet50 model was trained to classify three classes (healthy, arthritis, and arthrosis) from these images. Results show that the system can effectively distinguish healthy individuals from those with joint pathologies, achieving an overall test accuracy of approximately 64%. The model identified healthy hands with high confidence (100% sensitivity for the healthy class), but it struggled to differentiate between arthritis and arthrosis, often misclassifying one as the other. The prototype’s multiclass ROC (Receiver Operating Characteristic) analysis further showed excellent discrimination between healthy vs. diseased groups (AUC, Area Under the Curve ~1.00), but lower performance between arthrosis and arthritis classes (AUC ~0.60–0.68). Despite these challenges, the device demonstrates the feasibility of AI-assisted thermographic screening: it is completely non-invasive, radiation-free, and low-cost, providing results in real-time. In the discussion, we compare this thermography-based approach with conventional diagnostic modalities and highlight its advantages, such as early detection of physiological changes, portability, and patient comfort. While not intended to replace established methods, this technology can serve as an early warning and triage tool in clinical settings. In conclusion, the proposed prototype represents an innovative application of infrared thermography and deep learning for joint disease screening. With further improvements in classification accuracy and broader validation, such systems could significantly augment current clinical practice by enabling rapid and non-invasive early diagnosis of arthritis and arthrosis. Full article

Early and accurate diagnosis of dementia is essential to improving patient outcomes and reducing societal burden. The Mini-Mental State Examination (MMSE) is widely used to assess cognitive function, yet traditional statistical and machine learning approaches often face limitations in capturing nonlinear interactions and subtle decline patterns. This study developed a novel deep learning-based dementia prediction model using MMSE data collected from domestic clinical settings and compared its performance with traditional machine learning models. A notable strength of this work lies in its use of item-level MMSE features combined with explainable AI (SHAP analysis), enabling both high predictive accuracy and clinical interpretability—an advancement over prior approaches that primarily relied on total scores or linear modeling. Data from 164 participants, classified into cognitively normal, mild cognitive impairment (MCI), and dementia groups, were analyzed. Individual MMSE items and total scores were used as input features, and the dataset was divided into training and validation sets (8:2 split). A fully connected neural network with regularization techniques was constructed and evaluated alongside Random Forest and support vector machine (SVM) classifiers. Model performance was assessed using accuracy, F1-score, confusion matrices, and receiver operating characteristic (ROC) curves. The deep learning model achieved the highest performance (accuracy 0.90, F1-score 0.90), surpassing Random Forest (0.86) and SVM (0.82). SHAP analysis identified Q11 (immediate memory), Q12 (calculation), and Q17 (drawing shapes) as the most influential variables, aligning with clinical diagnostic practices. These findings suggest that deep learning not only enhances predictive accuracy but also offers interpretable insights aligned with clinical reasoning, underscoring its potential utility as a reliable tool for early dementia diagnosis. However, the study is limited by the use of data from a single clinical site with a relatively small sample size, which may restrict generalizability. Future research should validate the model using larger, multi-institutional, and multimodal datasets to strengthen clinical applicability and robustness. Full article

Introduction: The “revolving” door is a phenomenon that refers to the rehospitalization of older patients who, after being discharged, soon require specialized hospital care again. Unfortunately, the use of tools able to predict this phenomenon is still limited. The aim of this study was to highlight the validity of the Blaylock Risk Assessment Screening (BRASS) Scale in objectively assessing the risk of rehospitalization and mortality among older patients. Methods: Patients were classified as low, medium, or high risk using the BRASS scale. Adverse events (rehospitalization or death) were recorded at baseline and at 12 months. Kaplan–Meier curves evaluated survival and rehospitalization across risk groups, and ROC analysis assessed the BRASS Scale’s predictive value for mortality. Results: Out of 179 enrolled older adults (mean age 67.7 years), 54.2% were classified as low risk, 29.5% as medium, and 16.8% as high risk based on the BRASS Scale. High-risk patients had significantly higher mortality (HR: 4.40; 95% CI: 1.60–12.19, p = 0.004) and lower survival rates, while intermediate-risk patients had increased rehospitalization (HR: 2.11; 95% CI: 1.09–4.08, p = 0.02). The BRASS scale showed good predictive value for mortality (AUC 0.76). Conclusion: The BRASS Scale has a good predictive value for negative outcomes, and it confirms that a substantial proportion of older patients are at risk of future hospital readmissions and complex discharges. These findings underscore the importance of early post-discharge care planning and the implementation of protected discharge programs. Full article

Accurate recognition of estrus behavior in sows is of great importance for achieving scientific breeding management, improving reproductive efficiency, and reducing labor costs in modern pig farms. However, due to the evident spatiotemporal continuity, stage-specific changes, and ambiguous category boundaries of estrus behaviors, traditional methods based on static images or manual observation suffer from low efficiency and high misjudgment rates in practical applications. To address these issues, this study follows a video-based behavior recognition approach and designs three deep learning model structures: (Convolutional Neural Network combined with Long Short-Term Memory) CNN + LSTM, (Three-Dimensional Convolutional Neural Network) 3D-CNN, and (Convolutional Neural Network combined with Temporal Convolutional Network) CNN + TCN, aiming to achieve high-precision recognition and classification of four key behaviors (SOB, SOC, SOS, SOW) during the estrus process in sows. In terms of data processing, a sliding window strategy was adopted to slice the annotated video sequences, constructing image sequence samples with uniform length. The training, validation, and test sets were divided in a 6:2:2 ratio, ensuring balanced distribution of behavior categories. During model training and evaluation, a systematic comparative analysis was conducted from multiple aspects, including loss function variation (Loss), accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC curves. Experimental results show that the CNN + TCN model performed best overall, with validation accuracy exceeding 0.98, F1-score approaching 1.0, and an average AUC value of 0.9988, demonstrating excellent recognition accuracy and generalization ability. The 3D-CNN model performed well in recognizing short-term dynamic behaviors (such as SOC), achieving a validation F1-score of 0.91 and an AUC of 0.770, making it suitable for high-frequency, short-duration behavior recognition. The CNN + LSTM model exhibited good robustness in handling long-duration static behaviors (such as SOB and SOS), with a validation accuracy of 0.99 and an AUC of 0.9965. In addition, this study further developed an intelligent recognition system with front-end visualization, result feedback, and user interaction functions, enabling local deployment and real-time application of the model in farming environments, thus providing practical technical support for the digitalization and intelligentization of reproductive management in large-scale pig farms. Full article

Background/Objectives: The relationship between gut microbiota (GM) and microRNAs (miRs) related to lipid metabolism in individuals with metabolic syndrome (MetS) remains unclear. This pilot study examined the relationship between Bacteroidetes and Firmicutes abundance at the phylum level and the plasma levels of miR-122 and miR-370, both of which are associated with lipid metabolism, in Korean individuals with MetS and in healthy controls. We also evaluated the potential of these miRs as biomarkers for MetS. Methods: This study enrolled 7 individuals with MetS and 8 controls. The abundance of GM was analyzed by 16S rRNA amplicon sequencing. To evaluate the relationship between the dominant phyla in the 2 groups, the log ratio of Firmicutes to Bacteroidetes (F/B) was calculated using a centered log-ratio (CLR) transformation. The abundance of the 2 plasma miRs was also quantified by real-time quantitative PCR (RT-qPCR). Pearson’s and Spearman’s correlation analyses were then performed to evaluate the relationship between Bacteroidetes and Firmicutes abundance, the clinical parameters, and plasma levels of the 2 miRs. Additionally, the area under the curve (AUC) value of the receiver operating characteristic (ROC) curve was calculated to evaluate the potential of the 2 miRs as MetS biomarkers. Results: The 2 most abundant phyla were Bacteroidetes and Firmicutes. Bacteroidetes made up an average of 24.7% in the MetS group and 69.7% in the control group. Meanwhile, the average abundance of Firmicutes was 69.8% in the MetS group and 26.5% in the control group. The log F/B ratios in the MetS and control groups were 0.7 ± 0.5 and −0.4 ± 0.1 (p < 0.001), respectively. FDR analysis revealed significant correlations between Bacteroidetes abundance and BMI, DBP, FBG, total chol, insulin and HOMA-IR (FDR-adjusted p < 0.05), as well as between Firmicutes abundance and BMI, FBG, total chol, insulin and HOMA-IR (FDR-adjusted p < 0.05). Plasma levels of the 2 miRs differed significantly between the MetS and control groups: miR-122 (1.43 vs. 0.73; p = 0.0065) and miR-370 (1.39 vs. 0.83; p = 0.0089). The AUC values for miR-122 and miR-370 were 0.946 (p < 0.001) and 0.964 (p < 0.001), respectively. Pearson’s and Spearman’s correlation analyses revealed significant negative correlations between Bacteroidetes abundance and levels of miR-122 (p = 0.0048 and p = 0.0045, respectively) and miR-370 (p = 0.0003 and p < 0.0001, respectively), as well as significant positive correlations between Firmicutes abundance and levels of miR-122 (p = 0.0038 and p = 0.0027, respectively) and miR-370 (p = 0.0004 and p < 0.0001, respectively). However, as our exploratory findings were based on a small sample size, the high correlation results may partly reflect the separation between the MetS and control groups. Conclusions: Our exploratory findings suggest that the GM abundances of individuals with MetS may be significantly associated with plasma levels of miR-122 and miR-370, which are related to lipid metabolism. These miRs may therefore serve as potential MetS biomarkers. Full article

Background: Glaucoma is a multifactorial optic neuropathy and the leading cause of irreversible blindness worldwide. Vascular mechanisms, including impaired perfusion of the optic nerve head, are increasingly recognized as contributors to disease progression. Optical coherence tomography angiography (OCTA) enables non-invasive assessment of retinal and choroidal microvasculature, including peripapillary microvasculature dropout (MvD), which may serve as a marker of glaucomatous damage. Methods: A cross-sectional case–control study was conducted, including patients with primary open-angle glaucoma (OAG) and healthy controls. All participants underwent a comprehensive ophthalmic evaluation and OCTA imaging using the PLEX Elite 9000 system. Peripapillary vessel density (pVD), flow index (pFI), peripapillary choroidal thickness (PCT), β-zone parapapillary atrophy (β-PPA), and choroidal vascular indices were measured. MvD was defined as the complete absence of microvasculature within the β-PPA boundary. Statistical analyses included univariate and multivariate regression models to examine variables associated with PCT and to assess the association between MvD and visual field mean defect (MD), as well as other glaucoma characteristics. ROC curve analysis was performed to evaluate the ability of MvD to discriminate between different levels of visual field defects. Results: A total of 87 eyes (41 glaucomatous, 46 controls) were analyzed. Glaucoma patients exhibited significantly lower pVD, pFI, PCT, and choroidal vascular indices compared to the controls. MvD was detected in 10 glaucomatous eyes and was associated with a larger β-PPA area, smaller choroidal luminal and stromal areas, and worse mean deviation (MD) values. Multivariate regression showed that the number of ocular hypotensive treatments and StructureIndex variables were significantly associated with PCT (adjusted R² = 0.14). Logistic regression analysis identified MD, MD slope, and β-PPA area as variables significantly associated with the presence of MvD. ROC analysis showed that the presence of MvD had good discriminatory ability for visual field mean defects (MDs) (AUC = 0.77, 95% CI: 0.69–0.87; p = 0.005). Conclusions: Peripapillary MvD detected by OCTA is associated with reduced choroidal vascularity, increased β-PPA, and greater visual field deterioration in glaucoma patients. MvD may serve as a structural marker associated with functional deterioration in glaucoma patients. Full article

Background: Chimeric antigen receptor (CAR) T-cell therapy has revolutionized treatment of relapsed/refractory large B-cell lymphoma (LBCL), but its administration is often complicated by cytokine release syndrome (CRS). Interleukin-6 (IL-6) is widely used to monitor CRS, though its clinical value diminishes after tocilizumab administration. We aimed to evaluate serum amyloid A (SAA), a dynamic acute-phase reactant, as a treatment-independent biomarker of inflammation and toxicity in CAR-T recipients. Methods: This retrospective study included 43 adults with LBCL treated with axicabtagene ciloleucel. SAA and other inflammatory markers were assessed from lymphodepletion through day +11 post-infusion. CRS and ICANS were graded per ASTCT criteria. Statistical analyses included Mann–Whitney U tests, Spearman’s correlation, and ROC curve analysis to evaluate predictive performance. Results: SAA levels peaked at day +4 and normalized by day +11, displaying wave-like kinetics. Levels were significantly higher in patients with any-grade CRS at early timepoints but showed no association with ICANS. SAA correlated strongly with CRP, suPAR, sST2, fibrinogen, ferritin, procalcitonin, and IL-6. Compared to IL-6, SAA was more predictive of CRS at day +2 and +4, and unaffected by tocilizumab. Baseline SAA also correlated with the mEASIX score, suggesting linkage to endothelial stress. Non-responders at 3-month PET had higher baseline SAA than responders (196.0 vs. 17.7 mg/L, p = 0.036), with ROC analysis yielding an AUC of 0.74 and an optimal threshold of 79.8 mg/L. Conclusions: SAA is a robust and dynamic marker of systemic inflammation, with potential utility in both toxicity monitoring and response prediction in the CAR-T setting. Its independence from IL-6 modulation positions it as a promising biomarker for future integration into clinical algorithms. Full article

Iron overload-driven liver fibrosis is a major concern in β-thalassaemia patients, but non-invasive or minimally invasive biomarkers for fibrosis staging remain limited. This study evaluated five plasma microRNAs (let-7a, miR-21, miR-29a, miR-34a, and miR-122) as potential markers for distinguishing liver fibrosis stages in β-thalassaemia. Plasma samples from 40 patients with fibrosis stages F0–F1 to F4 were analysed using RT-qPCR, normalised against the arithmetic mean of reference miRNAs miR-16 and miR-221. Expression levels of candidate miRNAs showed no statistically significant variation across stages, and logistic regression and ROC analyses revealed fair discriminatory performance for individual miRNAs and their combinations in selected stage comparisons. Notably, while for the discrimination of different fibrosis stages all five candidate miRNAs tested showed fair area-under-the-curve values between 0.7 and 0.8 individually and up to 0.917 in combination, none of these findings reached statistical significance. These results suggest that while the selected set of miRNAs reflects liver injury, its performance for precise fibrosis staging in β-thalassaemia is limited. A key cause for the low discriminatory power of these miRNAs may be the overall change of the blood miRNA transcriptome in haemoglobinopathies. The results indicate the need for validation in larger cohorts based on larger miRNA panels or the use of alternative source materials to improve diagnostic performance. Full article