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Background: Steroid-resistant nephrotic syndrome (SRNS), commonly caused by focal segmental glomerulosclerosis (FSGS), is associated with progression to stage 5 chronic kidney disease, requirement for kidney replacement therapy and a risk of disease recurrence post-kidney transplantation. Ofatumumab (OFA) is a fully humanised monoclonal antibody to CD20, with similar mechanisms of action to rituximab (RTX).
Methods: We report a case series of seven UK patients (five paediatric, two adult), all of whom developed FSGS recurrence after kidney transplantation and received OFA as part of their therapeutic intervention. All also received concomitant plasmapheresis. The 2-year outcome of these seven patients is reported, describing clinical course, kidney function and proteinuria.
Results: Four patients (all paediatric) achieved complete urinary remission with minimal proteinuria 12 months post-treatment. Three of those four also had normal graft function. Two patients showed partial remission-brief improvement to non-nephrotic proteinuria (197 mg/mmol) in one patient, maintained improvement in kidney function (estimated glomerular filtration rate 76 ml/min/1.73 m) in the other. One patient did not demonstrate any response.
Conclusions: OFA may represent a useful addition to therapeutic options in the management of FSGS recurrence post-transplantation, including where RTX has shown no benefit. Concomitant plasmapheresis in all patients prevents any definitive conclusion that OFA was the beneficial intervention.
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http://dx.doi.org/10.1007/s00467-021-05248-9 | DOI Listing |
J Neurointerv Surg
September 2025
Cerebrovascular and Neuro-Intervention Department, Zhangzhou Municipal Hospital of Fujian Province and Zhangzhou Affiliated Hospital of Fujian Medical University, Zhangzhou, Fujian, China
Background: Remote live-streamed training in endovascular thrombectomy (EVT) is a novel educational strategy. This study evaluated the dose-response relationship between training duration and clinical outcomes, and explored mediation pathways.
Methods: In a prospective cohort study, 1046 participants received remote EVT training, with 1010 completing follow-up.
J Safety Res
September 2025
School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK 73019, United States; School of Industrial and Systems Engineering, University of Oklahoma, Norman, OK 73019, United States. Electronic address:
Introduction: The Highway Safety Manual (HSM) offers crash prediction models for estimating the number and severity of crashes for several facility types. However, since these models were developed using data from selected states within the United States, they should be calibrated when applied to a new jurisdiction. The HSM recommended using a scalar calibration factor to calibrate the prediction models.
View Article and Find Full Text PDFComput Methods Programs Biomed
August 2025
The Institute of Cancer Research, London, UK. Electronic address:
Background And Objective: Apparent Diffusion Coefficient (ADC) values and Total Diffusion Volume (TDV) from Whole-body diffusion-weighted MRI (WB-DWI) are recognised cancer imaging biomarkers. However, manual disease delineation for ADC and TDV measurements is unfeasible in clinical practice, demanding automation. As a first step, we propose an algorithm to generate fast and reproducible probability maps of the skeleton, adjacent internal organs (liver, spleen, urinary bladder, and kidneys), and spinal canal.
View Article and Find Full Text PDFIEEE Trans Pattern Anal Mach Intell
September 2025
Camouflaged Object Segmentation (COS) faces significant challenges due to the scarcity of annotated data, where meticulous pixel-level annotation is both labor-intensive and costly, primarily due to the intricate object-background boundaries. Addressing the core question, "Can COS be effectively achieved in a zero-shot manner without manual annotations for any camouflaged object?", we propose an affirmative solution. We analyze the learned attention patterns for camouflaged objects and introduce a robust zero-shot COS framework.
View Article and Find Full Text PDFRadiol Artif Intell
September 2025
Department of Radiology, Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, No. 197 Ruijin Er Road, Shanghai 200025, China.
Purpose To assess the effectiveness of an explainable deep learning (DL) model, developed using multiparametric MRI (mpMRI) features, in improving diagnostic accuracy and efficiency of radiologists for classification of focal liver lesions (FLLs). Materials and Methods FLLs ≥ 1 cm in diameter at mpMRI were included in the study. nn-Unet and Liver Imaging Feature Transformer (LIFT) models were developed using retrospective data from one hospital (January 2018-August 2023).
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