Search Results (846)

Objectives: This study evaluates the protocol-level image quality and quantitative diffusion metrics of a clinically implemented deep-learning–reconstructed readout-segmented echo-planar imaging protocol with water-excitation spectral fat saturation (DL-rs-EPI with WEXfs) compared with conventional rs-EPI using spectral attenuated inversion recovery (SPAIR) at 3 T. Methods: Overall, 80 patients underwent breast magnetic resonance imaging (MRI) with both conventional rs-EPI with SPAIR and DL-rs-EPI with WEXfs protocols (b-values: 0, 800, and 1200 s/mm²). ROI-based relative image-quality metrics, including signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and lesion contrast, were assessed at b = 800 and b = 1200 s/mm²; apparent diffusion coefficient (ADC) values were calculated using multi-b-value data. Fat suppression, background diffusion signal, lesion conspicuity, and artifact severity were qualitatively evaluated. A temperature-controlled diffusion phantom (CaliberMRI) was scanned; ADC values were compared with reference values at 24 °C. Results: DL-rs-EPI with WEXfs demonstrated higher ROI-based relative SNR estimates (b800: 5.79 vs. 5.28; b1200: 5.41 vs. 4.94; p < 0.001) and CNR estimates (b800: 3.35 vs. 3.12, p = 0.024; b1200: 3.67 vs. 3.37, p = 0.001), with unchanged lesion contrast. Tumor ADC values were comparable between protocols, whereas normal fibroglandular tissue ADC values were slightly higher, and ADC contrast increased with DL-rs-EPI with WEXfs. Phantom ADC values from both protocols closely matched reference values at 24 °C, without significant differences. DL-rs-EPI with WEXfs demonstrated more homogeneous fat suppression and reduced background diffusion signal, with comparable lesion conspicuity and artifact severity. Conclusions: The combined DL-rs-EPI with WEXfs protocol demonstrated improved qualitative and relative quantitative image quality while preserving tumor ADC measurements. As a protocol-level evaluation, these composite improvements support its clinical feasibility for high-quality breast DWI without implying the isolated effect of DL reconstruction alone. Full article

Background/Objectives: Absent septum pellucidum (ASP) is a rare fetal midline brain finding that may occur in isolation or alongside broader central nervous system (CNS) malformations, genetic disorders, or septo-optic dysplasia (SOD). Accurate prenatal diagnosis and counseling remain challenging because apparently isolated ASP may be reclassified following fetal magnetic resonance imaging (MRI), postnatal neuroimaging, or specialist assessment. This structured narrative review aimed to synthesize current evidence on prenatal imaging findings, associated anomalies, genetic evaluation, and postnatal outcomes in fetuses with ASP. Methods: This structured narrative review used PRISMA-informed reporting. PubMed and Google Scholar were searched for full-text English-language studies published from 2014 through the updated search date (8 June 2026). Data on gestational age at diagnosis, imaging classification, associated anomalies, genetic testing, postnatal assessment, and neurodevelopmental, ophthalmological, and endocrine outcomes were extracted. Study methodological quality was appraised using Joanna Briggs Institute tools. Results: Seven studies comprising 342 fetal ASP cases were included. Of these, 94 cases (27.5%) were classified as isolated ASP prenatally, but only 57 remained isolated postnatally when follow-up data were available. SOD was confirmed after birth in 11 of 94 (11.7%) fetuses with prenatally isolated ASP. As definitions, imaging protocols, genetic testing strategies, and follow-up duration differed substantially across studies, these pooled values are descriptive observations rather than formal quantitative estimates. Conclusions: ASP is a heterogeneous prenatal finding. The prognosis is most favorable when ASP remains isolated following a detailed prenatal and postnatal evaluation. Multidisciplinary follow-up involving fetal medicine, neuroradiology, genetics, ophthalmology, endocrinology, and neurology is essential for risk stratification and counseling. Full article

Background: Anti-amyloid monoclonal antibodies represent the first disease-modifying therapeutic strategy targeting amyloid-β pathology in early Alzheimer’s disease (AD). Although several agents have demonstrated the ability to reduce cerebral amyloid burden, their clinical efficacy and safety remain subjects of substantial scientific and regulatory debate. This study aimed to synthesize randomized evidence evaluating the benefit–risk profile of anti-amyloid monoclonal antibodies in biomarker-confirmed early AD. Methods: A systematic review and classical pairwise meta-analysis of randomized controlled trials (RCTs) was conducted following the PRISMA 2020 guidelines. PubMed/MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials were searched for phase III placebo-controlled trials evaluating lecanemab, donanemab, aducanumab, and gantenerumab in patients with mild cognitive impairment due to AD or mild AD dementia with biomarker confirmation of amyloid pathology. The primary outcome was change from baseline in the Clinical Dementia Rating–Sum of Boxes (CDR-SB) at the longest available follow-up. Safety outcomes included amyloid-related imaging abnormalities with edema or effusion (ARIA-E), amyloid-related imaging abnormalities with hemorrhage (ARIA-H), serious adverse events, and treatment discontinuation. Random-effects meta-analyses were performed. Results: Six randomized comparisons derived from four phase III trials involving 7695 participants met the eligibility criteria. Anti-amyloid monoclonal antibodies were associated with a statistically significant slowing of clinical progression compared with placebo (pooled mean difference in CDR-SB: −0.42 points; 95% CI −0.59 to −0.25; I² = 78%). The observed effect was primarily driven by trials of lecanemab and donanemab, whereas aducanumab demonstrated discordant results across trials and gantenerumab showed no clinically meaningful benefit. Despite statistical significance, the magnitude of the pooled effect approached the lower boundary of the minimal clinically important difference reported for CDR-SB in early AD. Treatment was associated with a markedly increased risk of ARIA-E (pooled risk ratio 10.1; 95% CI 7.8–13.0), with moderate heterogeneity across studies. Most ARIA-E events were asymptomatic and detected through protocol-mandated MRI monitoring. Conclusions: In biomarker-confirmed early Alzheimer’s disease, anti-amyloid monoclonal antibodies produce a statistically significant but modest slowing of clinical decline accompanied by a substantially increased risk of ARIA. The benefit–risk profile appears heterogeneous across individual antibodies and may reflect pharmacological differences in amyloid targeting and clearance mechanisms. These findings support cautious, individualized use of anti-amyloid therapies and highlight the need for longer-term studies to determine whether short-term slowing of decline translates into clinically meaningful disease modification. Full article

Background: The RAPIDO trial demonstrated that short-course radiotherapy (5 × 5 Gy) followed by chemotherapy and surgery decreased disease-related treatment failure compared to long-course chemoradiotherapy. However, at 5-year follow-up, an increase in locoregional recurrence was observed. A strategy to improve local control is to increase radiotherapy dose. We present a retrospective analysis of a patient cohort treated with intensified MRI-guided radiotherapy. Methods: Between November 2021 and January 2024, 28 patients with cT3-4 N0-2 rectal cancer were included. Treatment consisted of adaptive MRI-guided external beam radiotherapy 5 × 5 Gy on the mesorectum with a simultaneous integrated boost (SIB) of 5 × 6 Gy on the gross tumor volume (GTV) followed by chemotherapy consisting of 18 weeks of CAPOX or FOLFOX. In case of a complete clinical response (cCR) following neoadjuvant treatment, a watchful-waiting approach was considered. The primary outcome was local and distant recurrence. Results: 27 patients were included in the analysis, with a median follow-up of 39 months. One patient was excluded due to loss to follow-up. Six patients had a pathological complete response (pCR) on surgery, and seven in the watch-and-wait protocol had persistent cCR. Locoregional failure was observed in one patient and four patients developed distant metastasis, leading to death in one patient. Conclusions: Total neoadjuvant treatment (TNT) in locally advanced rectal cancer using an MRI-guided integrated boost on the GTV is tolerable and resulted in a high rate of pCR (22%) and persistent cCR (26%), with limited locoregional failure (4%) and distant relapse (15%). Full article

Shear wave elastography (SWE) is an increasingly investigated ultrasound-based technique in musculoskeletal imaging that provides quantitative information on tissue stiffness and biomechanical properties. This narrative review aims to summarize the basic principles, technical considerations, current clinical applications, limitations, and future perspectives of SWE in musculoskeletal imaging. Unlike conventional grayscale and Doppler ultrasonography, which mainly assess morphology and vascularity, SWE may provide additional functional information in major musculoskeletal tissues, including tendons and ligaments, skeletal muscles, peripheral nerves, fibrocartilaginous structures, plantar fascia, and selected soft tissue lesions. Current evidence suggests potential roles for SWE in detecting early biomechanical alterations, assessing disease severity, differentiating symptomatic from asymptomatic tissues, and monitoring response to treatment or rehabilitation. However, musculoskeletal tissues are anisotropic, viscoelastic, and position-dependent; as a result, SWE measurements are influenced by acquisition-related factors, tissue biomechanics, positioning and loading conditions, region of interest (ROI) placement, tissue depth, and device-related variability. For this reason, SWE findings should not be interpreted as standalone diagnostic criteria but should be considered together with clinical findings, conventional ultrasonography, MRI, electrophysiology, histopathology, and patient-centered outcomes when appropriate. This review highlights the need for tissue-specific measurement protocols, standardized reporting, normative reference data, inter-vendor harmonization, and longitudinal validation against clinically meaningful outcomes before SWE can be more reliably integrated into routine musculoskeletal imaging and rehabilitation practice. Full article

Advanced diffusion MRI techniques, particularly intravoxel incoherent motion (IVIM) and diffusion tensor imaging (DTI), have emerged as promising functional imaging tools for improving cervical cancer assessment beyond conventional anatomical MRI. This narrative review summarizes current evidence on the clinical utility of these diffusion-based techniques for tumor characterization, local staging, parametrial invasion, lymph node evaluation, treatment response monitoring, and emerging radiomics applications. Across studies, diffusion-related parameters, especially the apparent diffusion coefficient (ADC) and pure molecular diffusion coefficient (D), tend to be lower in malignant cervical tissues and correlate with increased cellularity, higher tumor grade, and more aggressive disease features. IVIM metrics appear especially useful for differentiating cervical cancer from normal tissue, predicting pelvic lymph node involvement, and detecting early treatment response to chemoradiotherapy or neoadjuvant chemotherapy before substantial morphological regression occurs. In contrast, DTI remains less extensively investigated; however, preliminary findings suggest potential value for evaluating parametrial invasion, stromal disruption, tumor grade, and lymph node metastasis, particularly when integrated with IVIM-derived indices. Although diffusion-derived radiomics may further support risk stratification and treatment-response prediction, the evidence base remains limited by small cohorts, single-center designs, methodological heterogeneity, and insufficient external validation. Overall, IVIM and DTI provide valuable non-invasive insight into cervical cancer biology, but standardized acquisition protocols, reproducible thresholds, and multicenter validation are needed before routine clinical implementation. Full article

Background: Radiology underpins diagnosis and treatment across pediatrics, yet most artificial intelligence (AI) tools are developed for adults and validated on adult datasets only. Of more than 200 AI systems cleared by the United States (U.S.) Food and Drug Administration (FDA), only about 3% include pediatric validation. Because children differ from adults in anatomy, physiology, pathology, epidemiology, and imaging protocols, adult-trained models often perform sub-optimally in pediatric settings. Methods: A narrative review of peer-reviewed literature from 2000 to 2025 was conducted using PubMed, MEDLINE, Google Scholar, and Scopus. Studies involving AI applications in pediatric X-ray, ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), echocardiography, and point-of-care ultrasound with quantitative performance metrics were included. Findings were synthesized by imaging modality, clinical task, and differences between high-income countries (HICs) and low- and middle-income countries (LMICs). Results: AI demonstrated strong performance across multiple pediatric imaging tasks. In X-ray interpretation, AI detected fractures with area under the curve (AUC) values up to 0.96 (sensitivity, 90.8%; specificity, 88.7%). Pneumonia classification achieved 76.5% accuracy, and foreign body aspiration detection showed 95.3% specificity in HICs. In ultrasound, AI improved junior sonographers’ detection of intussusception (AUC 0.857 to 0.966) and reduced scan time by more than 50%. AI-assisted bone age estimation achieved a mean error of 0.39 years. In echocardiography, AI-derived ejection fraction showed excellent agreement with experts’ interclass correlation coefficient (ICC 0.983), and AI support improved atrioventricular septal defect detection (84.4% to 86.5%). In MRI, the use of AI enhanced lesion detection and supported quantitative analysis. Deep-learning models trained on routine T1- and T2-weighted sequences predicted liver stiffness across multi-site datasets, while advanced neuroimaging pipelines improved the identification of subtle epileptogenic lesions that are often missed on conventional pediatric MRI. However, adult-trained models showed limited generalizability to children. Still, excluding children under the age of two years improved the reading accuracy of pediatric chest X-rays (CXRs) by adult-trained models from 88% to 97%. AI faces challenges beyond the development of age-specific models. Substantial heterogeneity, limited pediatric-specific datasets, and unresolved medicolegal responsibility further restrict adoption worldwide. Challenges are amplified in LMICs, where unstable electricity, limited radiology resources, weak digital infrastructure, and scarce pediatric providers limit implementation. Additionally, many large language models underperform and lack inclusive algorithms suitable for pediatric radiology in many LMICs. Conclusions: AI can enhance diagnostic accuracy, efficiency, and access to pediatric imaging, particularly in resource-limited settings, through task-shifting and decision support. However, it cannot replace pediatric radiologists as of today. Safe adoption requires pediatric-specific model development, standardized validation metrics, diverse datasets that include LMIC populations, stronger digital infrastructure, robust radiologist training in AI capabilities, and the establishment of clear guidelines and medicolegal policies. Full article

In many university and healthcare projects, models are built for very different data types such as tables, institutional time series, and medical images, but they are deployed as separate applications. In this work, that separation made testing and maintenance difficult because each module had its own pipeline and runtime requirements. This paper presents an integrated AI lakehouse-style implementation that runs three model pipelines inside one containerized backend. For medical imaging, we used MRI datasets from IEEE DataPort: a four-class classification set with 7012 images (5708 train/1304 test) and a segmentation set with 3063 image–mask pairs. The classification model (ResNet50 transfer learning) is evaluated using a proper train–validation–test protocol across multiple splits (80/10/10, 70/10/20, 60/10/30, and 10/30/60), achieving a test accuracy of 99.00% under the standard 80/10/10 split. Additionally, a patient-level evaluation is conducted using an external glioma dataset to provide a more realistic assessment without data leakage. The segmentation model (DeepLabV3-ResNet50) achieved 83.09% validation mIoU and 88.79% Dice score. For university KPI forecasting, we used annual IPEDS and NSF HERD data from 2010 to 2023 for three universities (BSU, EOU, and UAB). To examine the effect of preprocessing on forecasting performance, two case studies are conducted. In the first case, linear interpolation is applied to generate semester-level data. In the second case, the original annual data is used directly without interpolation. Random Forest regression and ARIMA models are evaluated using MAE, RMSE, MAPE, and $R^2$. The results showed that interpolation improved apparent forecasting performance due to smoothing, while evaluation on the original annual data provided a more realistic assessment of model behavior. To further validate the framework on a larger dataset, an additional case study is conducted using a student dropout dataset. For water potability, we trained and compared multiple tabular classifiers on a large dataset (1,048,575 samples). A Random Forest model (100 trees, max depth 10) achieved 85.86% test accuracy and high recall for unsafe samples (0.8447). All modules are served via FastAPI and deployed together using Docker, with workflow automation routing requests to the correct endpoint. System-level benchmarking indicates that the backend maintains stable throughput and latency under concurrent requests. Full article

Background/Objectives: This preliminary pilot case series aims to evaluate the feasibility and temporal evolution of pain and function following an ultrasound-guided infiltration technique with hyaluronic acid and methylprednisolone in a specific patient population with Temporomandibular Disorders (TMD) characterized by MRI-confirmed retrodiscal tissue hyperemia. Given the absence of a control group, this study represents a preliminary exploration of a clinical approach utilizing individualized interocclusal devices during infiltration. Methods: Twenty-eight patients (16 females, 12 males) with TMD and MRI evidence of retrodiscal tissue hyperemia were enrolled in this prospective, uncontrolled study. A unique protocol was employed, utilizing individualized interocclusal devices to optimize joint space access during bilateral ultrasound-guided infiltration of a mixture containing low-molecular-weight hyaluronic acid, lidocaine, and methylprednisolone acetate. Pain intensity (VAS 0–100 mm) and associated symptoms (tinnitus, vertigo, headache, joint clicking) were assessed at baseline and at 30, 60, and 90 days’ follow-up. Results: A statistically significant temporal reduction in pain was observed at all follow-up points (p < 0.001), with the mean VAS score decreasing from 70.5 ± 11.4 mm at baseline to 43.0 ± 11.1 mm at 90 days. Joint clicking disappeared in 80% of patients immediately after treatment. Conclusions: The ultrasound-guided infiltration technique, combined with personalized interocclusal support, demonstrated preliminary feasibility and short-term temporal improvement in pain and joint clicking in this specific patient cohort. Due to the lack of a control group and the multimodal nature of the intervention, these findings should be considered preliminary and do not allow for causal inferences regarding the efficacy of individual components. Full article

Background/Objectives: Multiple sclerosis (MS) is an autoimmune neuroinflammatory disease affecting 2.9 million worldwide. Current immunosuppressive treatments offer limited neuroprotection and often cause adverse effects. Polyphenols with antioxidant and anti-inflammatory properties have been investigated as adjuncts in MS. Methods: On 19 June 2025, Embase, Medline, and ClinicalTrials.gov were systematically searched. Eligible clinical trials assessing polyphenol supplementation in MS were included. Outcomes of interest were Expanded Disability Status Scale (EDSS), annualised relapse rate (ARR), magnetic resonance imaging (MRI) changes, safety, and tolerability. Risk of bias was evaluated using the Cochrane tool, and certainty of evidence was appraised with Grading of Recommendations Assessment, Development, and Evaluation (GRADE). The protocol was registered with PROSPERO (CRD420251052042). Results: Of 870 records identified, 13 trials (n = 785) met inclusion. Nanocurcumin consistently improved EDSS in relapsing–remitting MS (3 trials, n = 150; p = 0.039–0.041), while epigallocatechin-3-gallate, silymarin (SM), cranberry extract, and bio-enhanced curcumin extract (BCM-95) curcumin showed no significant impact on disability, relapse rates, or MRI outcomes. Intervention adverse events were generally mild. SM showed potential hepatoprotective effects. Risk of bias was determined as low risk for seven of the trials and of some concern for five of the studies. Most often raising concerns because of selective reporting. Certainty of evidence, assessed using GRADE, was generally moderate, indicating some uncertainty regarding the outcomes. Meta-analysis was not possible due to the heterogeneity of included studies. Conclusions: Nanocurcumin may contribute to improvements in disability outcomes in Relapse Remitting Multiple Sclerosis (RRMS), whereas other polyphenols lack consistent efficacy. However, the evidence base remains limited by small sample sizes and methodological concerns. Larger, multicentre randomised controlled trials are required to establish optimal dosing, long-term safety, and therapeutic potential. Full article

Background/Objectives: Preoperative membranous urethral length (MUL) is a predictor of post-prostatectomy urinary incontinence. However, measurement inconsistencies have hindered the establishment of ethnically specific clinical thresholds. We identified precise and internally validated MUL cutoff values for persistent incontinence at 6 and 12 months in a Korean cohort using a standardized measurement protocol. Methods: We retrospectively analyzed 151 patients who underwent robot-assisted radical prostatectomy (RARP) between 2022 and 2024. Preoperative MUL was measured using a 3-axis cross-reference system (CRS) on 3.0T mpMRI. Continence was defined as 0 or 1 safety pad per day. Independent predictors were identified via multivariable logistic regression, and the optimal cutoff values were determined using the Youden index with 1000-iteration bootstrap validation. Results: Preoperative MUL was significantly longer in continent than in incontinent patients at 6 (16.7 vs. 13.7 mm) and 12 months (16.7 vs. 11.3 mm; both p < 0.001). In the multivariable analysis, MUL was the only significant independent predictor for persistent incontinence (6 months: OR 0.798, p < 0.001; 12 months: OR 0.586, p < 0.001). The univariable AUROCs for predicting persistent incontinence were 0.707 (6 months) and 0.875 (12 months), whereas the multivariable AUROCs were 0.756 and 0.883, respectively. Optimal cutoff values from bootstrap were 14.00 mm (95% confidence interval [CI], 11.43–16.68) for 6-month and 12.60 mm (95% CI, 11.43–14.37) for 12-month persistent incontinence. Conclusions: Using a standardized CRS protocol, this study provides validated population-specific MUL thresholds for predicting persistent incontinence after RARP in Korean men, offering a pragmatic framework for preoperative risk stratification and evidence-based patient counseling. Full article

Background/Objectives: Atrial fibrillation (AF) is the most common supraventricular arrhythmia and one of the most commonly encountered heart conditions in clinical practice. Emerging evidence suggests a significant role of inflammation, endothelial disfunction and extracellular matrix (ECM) remodeling in the pathogenesis of AF. Population studies have also suggested an association between AF and cerebral small vessel disease (CSVD), with growing evidence indicating that the burden of certain markers of CSVD is greater in women. However, the association between female sex and CSVD remains poorly understood. The aim of this study was thus to investigate the role of female sex in the association between circulating biomarkers and the presence of CSVD in AF patients undergoing oral anticoagulant therapy. Methods: The Strat-AF study is an observational, prospective, single-center, hospital-based study enrolling elderly patients with AF. Results refer to 170 patients (59 women and 111 men). Recruited patients are evaluated by means of a comprehensive protocol, with clinical, cerebral magnetic resonance imaging (MRI) and circulating biomarker assessments. Results: From a multivariate logistic regression analysis adjusted for multiple confounders, independent predictors were: in women, elevated vWF levels for the presence of lacunar infarcts [OR 3.24 (1.23–8.55), p = 0.018], elevated MMP-12, TIMP-1, TIMP-2, and TIMP-4 levels for the presence of CMBs [OR 7.76 (1.60–37.69), p = 0.021; OR 1.90 (1.02–3.52), p = 0.042; OR 2.46 (1.27–4.80), p = 0.008; and OR 2.36 (1.12–4.95), p = 0.023, respectively], elevated IL-6 and MMP-2 levels for the presence of WMH [OR 10.65 (1.31–86.67), p = 0.027; OR 3.36 (1.23–9.15), p = 0.018, respectively] and elevated MMP-12 and TIMP-2 levels for the presence of bgEPVS [OR 2.57 (1.22–5.93), p = 0.027; OR 2.15 (1.03–4.53), p = 0.043, respectively]; and in men: elevated TIMP-1 levels for the presence of WMH [OR 2.10 (1.08–4.08), p = 0.030], elevated TIMP-1 levels for the presence of bgEPVS [OR 2.20 (1.11–4.38), p = 0.025] and elevated TIMP-1 levels for SVDs positivity [OR 7.25 (2.18–24.15), p = 0.001]. Conclusions: These results from the Strat-AF study demonstrated that a complete biohumoral and instrumental assessment can jointly identify female patients with AF at higher risk of CSVD. These findings pave the way for the implementation of clinical protocols incorporating brain MRI and circulating biomarkers as potential innovative tools for an increasingly refined—and sex-specific—stratification of cardiovascular risk in AF patients undergoing oral anticoagulant therapy. Full article

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder for which early and accurate diagnosis remains a critical challenge. In this work, we propose a Multi-ROI Multimodal 3D Vision Transformer for AD classification that integrates structural MRI data with clinical and volumetric biomarkers within a unified attention-based framework. The proposed approach leverages anatomically guided multi-region-of-interest (ROI) decomposition to focus on disease-relevant brain structures, including the hippocampus, entorhinal cortex, fornix, and major cortical lobes. Each ROI is encoded using 3D tubelet embeddings, while clinical and volumetric features are transformed into feature-wise tokens, enabling seamless multimodal fusion through self-attention mechanisms. A hemisphere-aware selection strategy is introduced to identify the most discriminative ROI representations, enhancing both performance and interpretability. The model is evaluated on a merged multi-cohort dataset combining ADNI, AIBL, and OASIS using a 7-fold cross-validation protocol. Experimental results demonstrate that the proposed method achieves high classification performance, reaching an accuracy of 97.62% and an AUC of 0.9940, outperforming single-modality and whole-brain baselines. Furthermore, attention-based analysis provides interpretable insights into the relative importance of clinical and neuroanatomical features, revealing consistency with established AD biomarkers. These findings highlight the effectiveness of multimodal integration and ROI-based representation for robust and explainable AD classification. Full article

Anthropogenic emissions have caused the Antarctic ozone hole, a major global environmental crisis since the late 20th century. Although ozone recovery began in the early 21st century, substantial uncertainty remains regarding the timing of its return to pre-loss levels. This study innovatively develops a “model optimization–bias correction” framework based on spatial pattern (S1) and long-term trend (S2) metrics, assessing 17 Chemistry-Climate Model Initiative Phase 1 (CCMI-1) models using the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis for the climate (ERA5). Results: (1) Most models accurately reproduce the Antarctic ozone’s spatial distribution and long-term trends: MRI-ESM1r1 performs best for spatial patterns (S1 = 0.80), GEOSCCM for long-term trends (S2 = 0.82); EMAC-L90MA, UMSLIMCAT, etc., show poor spatial pattern performance (S1 < 0.30), while IPSL and EMAC-L90MA have large trend biases and underperform in trend simulation (S2 < 0.10). (2) Integrating S1 and S2 scores, the Preferred Multi-Model Ensemble comprising the top eight models (PMME8) minimizes ERA5 deviation, outperforming the multi-model ensemble (MME); the Combined Nonstationary Cumulative Distribution Function matching (CNCDFm) correction of this ensemble reduces systematic bias by 15–60%. (3) Antarctic ozone recovery time shows a gradual delay following optimal model selection and bias correction. PMME-adjusted projects recovery in October 2063 (2053–2072), later than MME (2052) and PMME (2058), with inter-member uncertainty narrowing from 43 years to 19 years. Similarly, this feature is also found for September, November, and the spring mean. This study provides a reliable methodological foundation for projections of Antarctic ozone recovery and offers scientific support for the compliance assessment and policy adjustment of the Montreal Protocol, thereby advancing environmental sustainability and global ozone governance. 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