General Electric (Spain)

Obesity can lead to various forms of brain damage and cognitive impairments. The neuropathological mechanism may involve brain iron deposition and changes in cerebral blood flow perfusion. This study aims to use Quantitative Susceptibility Mapping (QSM) and Arterial Spin Labeling (ASL) to quantitatively assess brain iron deposition and Cerebral Blood Flow (CBF) in patients with obesity, and to explore their patterns of alteration and association with cognitive impairment. 37 patients with obesity and 45 controls underwent 3.0 T MRI and cognitive assessment. We compared susceptibility and cerebral blood flow values within automatically segmented regions of the interest and analyzed associations with cognition using partial correlations. Patients displayed increased iron in the left nucleus accumbens and bilateral red nuclei (p = 0.001, p = 0.001, p < 0.001) and abnormal perfusion in the right cerebellum, right middle frontal gyrus, left putamen, and right anterior cingulate gyrus (p < 0.001). Furthermore, iron deposition correlated with cognitive impairment. This study suggests that obese patients may exhibit iron deposition and perfusion abnormalities in certain areas of the brain. Bilateral red nuclei iron deposition is closely related to cognitive impairment. The multimodal combined application of QSM and ASL provides imaging evidence for revealing the impact of obesity on brain structure and function, and also provides potential biomarkers for targeted interventions in the future.

Choroid plexus (CP) dysfunction may impair cerebrospinal fluid (CSF) turnover and perivascular glymphatic clearance, but whether CP microstructural injury correlates to cerebral small vessel disease (CSVD)-related brain alteration and cognitive performance remains unclear. We investigated whether imaging markers of perivascular fluid transport mediate the associations between CP alterations and CSVD pathology. CP microstructure was assessed by mean apparent propagator (MAP) diffusion imaging and χ-separation susceptibility mapping in 139 CSVD patients and 52 healthy controls. Perivascular clearance measures included the diffusion tensor image-along perivascular space (DTI-ALPS) index, basal ganglia free-water fraction (FW-BG), and perivascular space volume fraction (PVSVF-BG). Compared with controls, CSVD patients showed CP microstructural abnormalities and altered perivascular clearance markers, which was identified by random-forest model as predictors of CSVD severity (OOB-AUC = 0.755, 95%CI: 0.641-0.836). Mediation analysis revealed that imaging markers of perivascular fluid clearance significantly mediated the associations between CP alterations and structural brain injury (average causal mediation effect , ACME=-110.118 to 80.121, FDR-p < 0.05). Notably, PVSVF-BG was the only glymphatic imaging metric mediating the links of both CP volume and susceptibility with cognitive performance (ACME=-142.474 to 67.351, FDR-p < 0.05). These findings indicate that CP microstructural injury are present in mild CSVD with the absence of CP volumetric enlargement, and are associated with CSVD markers and cognitive performance, which are mediated by disrupted perivascular fluid transport, highlighting the CP-glymphatic axis as a candidate pathway for further longitudinal and mechanistic investigation.

BackgroundBiosimilars are increasingly used in rheumatology practice; however, knowledge gaps and regulatory uncertainties may affect their acceptance. This study assessed the knowledge, perceptions, and clinical practices related to biosimilars among rheumatologists in the Arab League of Associations for Rheumatology (ArLAR) countries.MethodsA web-based survey of 19 questions, adapted from a European Society for Medical Oncology (ESMO) tool, was distributed among ArLAR-affiliated rheumatologists between July 2024 and January 2025. The questionnaire assessed knowledge, attitudes, clinical use, and educational preferences. A French version was developed and validated by the study steering committee. Responses were analyzed using descriptive statistics.ResultsA total of 105 rheumatologists from various Arab countries participated in the survey. The mean age of participants was 43.6 ± 11.7 years, and 61.9% were females. Most participants were adult rheumatologists (94.3%), primarily from Iraq (37.1%), Morocco (19%), and Algeria (16.2%). While 85.7% reported using biosimilars in practice, only 50.5% correctly identified the formal definition. Comfort with EMA/FDA-approved biosimilars was high (mean score 4.22/5), while concerns remained about switching in stable patients and among multiple biosimilars. The majority (80%) expressed interest in additional biosimilar education, particularly on regulatory approval, extrapolation, and safety.ConclusionArLAR rheumatologists show high uptake of biosimilars but moderate understanding of key regulatory and clinical concepts. Regionally adapted educational programs are needed to support evidence-based biosimilar use and improve confidence in clinical decision-making.

BackgroundType 2 diabetes mellitus (T2DM) is often accompanied by cognitive dysfunction, though its underlying mechanisms remain unclear. Impaired glymphatic system function may contribute to cognitive impairment. Herein, we aimed to evaluate glymphatic activity in patients with T2DM using diffusion tensor imaging analysis along perivascular spaces (DTI-ALPS) and to explore its mediating role between white matter hyperintensities (Fazekas score) and Montreal Cognitive Assessment (MoCA) scores.MethodsThirty-eight patients with T2DM and 54 age- and sex-matched healthy controls underwent 3.0 T magnetic resonance imaging (MRI) and MoCA testing. The DTI-ALPS index was derived from diffusion tensor imaging data. Clinical variables included demographics, disease duration, metabolic indicators, and the homeostatic model assessment of insulin resistance (HOMA-IR) index. Group differences were analyzed using ANCOVA, correlations were assessed using Pearson analysis, and mediation analysis was used to evaluate the role of the DTI-ALPS index between Fazekas score and MoCA.ResultsThe DTI-ALPS index was significantly lower in patients with T2DM having cognitive impairment compared to both healthy controls and patients with T2DM without cognitive impairment (p < 0.001). MoCA scores positively correlated with the DTI-ALPS index (r = 0.860, p < 0.001), while HOMA-IR showed a negative correlation (r = −0.326, p < 0.05). Longer disease duration and higher Fazekas scores were associated with reduced DTI-ALPS index (p < 0.05). Mediation analysis indicated that the association between Fazekas score and MoCA was significantly and partially accounted for by the DTI-ALPS index in patients with T2DM (effect = −0.344, p < 0.05).ConclusionThe DTI-ALPS index was strongly associated with cognitive performance in patients with T2DM. Mediation analysis suggests that this relationship may reflect a mechanism in which impaired glymphatic function mediates the impact of white matter lesions on cognitive decline.

ABSTRACT As the circular economy transition gains traction in the healthcare sector, extending the lifespan of complex medical devices has become a key priority, given their high cost and the challenges associated with their technological sophistication. To effectively advance this transition, there is a growing need for a robust tool capable of assessing and enhancing the circularity of complex medical devices. In response, this study introduces the Medical Device Circularity Assessment Tool (MDCATool), a novel tool for identifying circular redesign strategies to improve the circularity performance of complex medical devices. The MDCATool draws on the hybrid “Closing‐Slowing Future‐Past” quadrant model, enriched with a hierarchy of product life extension strategies and associated environmental performance attributes. To demonstrate its applicability, the developed tool is employed to identify redesign strategies for an X‐ray tube, an integral component of a mammography system from a medical technology original equipment manufacturer. In addition, this study presents a circularity data checklist to support automated diagnostics and redesign decisions, forming the basis of a digital application for medical device assessment. The tool, designed for industry practitioners, supports evidence‐based decisions and innovation in circular medical device design, helping to improve the circularity performance of complex medical devices, including magnetic resonance imaging scanners, computed tomography systems, and X‐ray and ultrasound devices.

This study aimed to develop and validate a CT-based nomogram incorporating three-dimensional fractal dimension (FD 3D) to noninvasively predict tumor spread through air spaces (STAS) in stage IA lung adenocarcinoma. A retrospective analysis was performed on 110 patients with stage IA lung adenocarcinoma who underwent surgical resection. CT morphological features and fractal-dimension metrics were collected. Patients were categorized into STAS-positive (n = 48) and STAS-negative (n = 62) groups based on pathology. Univariate and multivariate logistic regression analyses were conducted to identify independent predictors of STAS. Receiver operating characteristic (ROC) curve analysis evaluated predictive performance, and a nomogram model was constructed and internally validated. Based on the nomogram score, patients were further stratified into low- and high-risk STAS groups using the optimal cutoff value determined by the maximum Youden index. Univariate analysis showed significant differences in consolidation-to-tumor ratio (CTR) (p < 0.001), morphological irregularity (p = 0.006), lobulation (p = 0.039), pleural indentation (p = 0.004), vascular convergence (p = 0.010), and FD 3D (p < 0.001) between groups. Multivariate analysis identified CTR, morphological irregularity, lobulation, and FD 3D as independent predictors of STAS in stage IA lung adenocarcinoma. The nomogram model achieved an area under the curve (AUC) of 0.894 (95%CI: 0.821-0.944; p < 0.001), with a sensitivity of 75.00% and a specificity of 90.32%. At the optimal cutoff value of 0.56, the model demonstrated a positive predictive value (PPV) of 85.71% in the high-risk group (n = 42, 38.18%) and a negative predictive value (NPV) of 82.35% in the low-risk group (n = 68, 61.82%), with significant differences in STAS prevalence between groups (85.71% vs. 17.65%, χ 2 = 46.18, p < 0.001). The CT-based nomogram integrating FD 3D and key imaging features can noninvasively predict STAS status in stage IA lung adenocarcinoma. This model shows promise for assisting surgical decision-making, though prospective studies are needed to validate its clinical utility.

Background The Healthy Minds Study (HMS) Questionnaire is an assessment tool that examines mental health, service awareness and help-seeking behaviour (including stigma and knowledge) among university students. Aims A shortage of tools specifically designed to cater to student populations has resulted in limited data from Arabic-speaking countries on mental health in university students. Our study aimed to describe the prevalence of mental health symptoms, service awareness, stigma and help-seeking attitudes among medical students at the United Arab Emirates (UAE) University. Method We conducted a cross-sectional survey of 87 medical students at the UAE University, using the HMS Questionnaire. Results A total of 73.6% reported at least mild depressive symptoms (Patient Health Questionnaire-9 (PHQ-9) ≥ 5), 35.8% met criteria for probable major depression (PHQ-9 ≥ 10), 46% screened positive for anxiety, 11.5% reported non-suicidal self-injury, 9.2% reported seriously thinking of suicide, 13.8% reported symptoms of an eating disorder, 8.1% agreed that they would think less of a person who has received mental health treatment and 69% agreed that most people would think less of a person who has received mental health treatment. Conclusions We found a high prevalence of mental health difficulties and a reluctance to report and seek help for mental health difficulties among medical students in the UAE, which may be associated with a negative perceived public stigma.

2D T2 FSE is an essential routine spine MRI sequence, allowing assessment of fractures, soft tissues, and pathology. Fat suppression using a DIXON-type approach (2D FLEX) improves water/fat separation. Recently, a deep learning (DL) reconstruction (AIR™ Recon DL, GE HealthCare) became available for 2D FLEX, offering increased signal-to-noise ratio (SNR), reduced artifacts, and sharper images. This study aimed to compare DL-reconstructed versus non-DL-reconstructed spine 2D T2 FLEX images for diagnostic image quality and quantitative metrics at 1.5T. Forty-one patients with clinically indicated cervical or lumbar spine MRI were scanned between May and August 2023 on a 1.5T Voyager (GE HealthCare). A 2D T2 FLEX sequence was acquired, and DL-based reconstruction (noise reduction strength: 75%) was applied. Raw data were also reconstructed without DL. Three readers (CAQ-neuroradiologist, PGY-6 neuroradiology fellow, PGY-2 radiology resident) rated diagnostic preference (0 = non-DL, 1 = DL, 2 = equivalent) for 39 cases. Quantitative measures (SNR, total variation [TV], number of edges, and fat fraction [FF]) were compared using paired t-tests with significance set at p < .05. Among evaluations, 79.5% preferred DL, 11% found images equivalent, and 9.4% favored non-DL, with strong inter-rater agreement (p < .001, Fleiss' Kappa = 0.99). DL images had higher SNR, lower TV, and fewer edges (p < .001), indicating effective noise reduction. FF remained statistically unchanged in subcutaneous fat (p = .25) but differed slightly in vertebral bodies (1.4% difference, p = .01). DL reconstruction notably improved image quality by enhancing SNR and reducing noise without clinically meaningful changes in fat quantification. These findings support the use of DL-enhanced 2D T2 FLEX in routine spine imaging at 1.5T. Incorporating DL-based reconstruction into standard spine MRI protocols can increase diagnostic confidence and workflow efficiency. Further studies with larger cohorts and diverse pathologies are warranted to refine this approach and explore potential benefits for clinical decision-making.