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Background Patients with end stage renal disease (ESRD) undergoing hemodialysis are at increased risk for infection and impaired vaccination responses. We analyzed overlap and influencing factors of vaccination responses against severe acute respiratory syndrome corona virus disease 2 (SARS-CoV-2) and Hepatitis B virus (HBV). Methods SARS-CoV-2 and HBV vaccination response was assessed in a cohort of German ESRD hemodialysis patients. Anti-HBs- and SARS-CoV-2 anti-S-IgG were analyzed by ELISA. Demographic and clinical data were extracted from clinical files. Results Sixty-four patients with complete information on HBV and SARS-CoV-2 vaccination responses were included. More than one-third (35.4%) of non-responders upon HBV vaccination were identified. Unresponsiveness after HBV and poor response after SARS-CoV-2 vaccination showed strong overlap, and overall, 70.3% of patients were classified into concordant HBV/SARS vaccination response groups. HBV vaccination non-responsiveness, but not poor SARS-CoV-2 post-vaccination immunity was associated with obesity, while poor SARS-CoV-2 vaccination responses were associated increased age. Conclusion Our findings confirm previous reports on impaired vaccination response in hemodialysis patients and show that post-vaccination humoral responses against SARS-CoV-2 and HBV display strong overlap in this vulnerable patient group. These results may help to adapt vaccination strategies in this highly vulnerable population. Trial registration : German Clinical Trial Registry, DRKS00021152.

New SARS-CoV-2 lineages continue to evolve and may exhibit new characteristics regarding host cell entry efficiency and potential for antibody evasion. Here, employing pseudotyped particles, we compared the host cell entry efficiency, ACE2 receptor usage, and sensitivity to antibody-mediated neutralization of four emerging SARS-CoV-2 lineages, KP.2, KP.2.3, KP.3, and LB.1. The XBB.1.5 and JN.1 lineages served as controls. Our findings reveal that KP.2, KP.2.3, KP.3, and LB.1 lineages enter host cells efficiently and in an ACE2-dependent manner, and that KP.3 is more adept at entering Calu-3 lung cells than JN.1. However, the variants differed in their capacity to employ ACE2 orthologues from animal species for entry, suggesting differences in ACE2 interactions. Moreover, we demonstrate that only two out of seven therapeutic monoclonal antibody (mAbs) in preclinical development retain robust neutralizing activity against the emerging JN.1 sublineages tested, while three mAbs displayed strongly reduced neutralizing activity and two mAbs lacked neutralizing activity against any of the lineages tested. Furthermore, our results show that KP.2, KP.2.3, KP.3, and LB.1 lineages evade neutralization by antibodies induced by infection or vaccination with greater efficiency than JN.1, particularly in individuals without hybrid immunity. This study indicates that KP.2, KP.2.3, KP.3, and LB.1 differ in ACE2 interactions and the efficiency of lung cell entry and suggest that evasion of neutralizing antibodies drove the emergence of these variants.

Two compounds from citrus peel, tangeretin (TAN) and 3′,4′,3,5,6,7,8‐heptamethoxyflavone (HMF), were investigated for their abilities to repair metabolic damages caused by an high‐fat diet (HFD) in C57BL/6J mice. In the first 4 weeks, mice were fed either a standard diet (11% kcal from fat) for the control group, or a HFD (45% kcal from fat) to establish obesity in three experimental groups. In the following 4 weeks, two groups receiving the HFD were supplemented with either TAN or HMF at daily doses of 100 mg/kg body weight, while the two remaining groups continued to receive the standard healthy diet or the nonsupplemented HFD. Four weeks of supplementation with TAN and HMF resulted in intermediate levels of blood serum glucose, leptin, resistin, and insulin resistance compared with the healthy control and the nonsupplemented HFD groups. Blood serum peroxidation (TBARS) levels were significantly lower in the TAN and HMF groups compared with the nonsupplemented HFD group. Several differences occurred in the physiological effects of HMF versus TAN. TAN, but not HMF, reduced adipocyte size in the mice with pre‐existent obesity, while HMF, but not TAN, decreased fat accumulation in the liver and also significantly increased the levels of an anti‐inflammatory cytokine, IL‐10. In an analysis of the metabolites of TAN and HMF, several main classes occurred, including a new set of methylglucuronide conjugates. It is suggested that contrasts between the observed physiological effects of TAN and HMF may be attributable to the differences in numbers and chemical structures of TAN and HMF metabolites. Tangeretin (TAN) and heptamethoxyflavone (HMF), and their metabolites, influence metabolic parameters in mice with pre‐existing obesity. Differences in the effects of HMF and TAN may be attributed to the very different profiles of metabolites of these two compounds. Unlike TAN, HMF had dramatic influence in alleviating liver steatosis in obese mice.

The largest bottleneck to the development of convolutional neural network (CNN) models in the computational pathology domain is the collection and curation of diverse training datasets. Training CNNs requires large cohorts of image data, and model generalizability is dependent on training data heterogeneity. Including data from multiple centers enhances the generalizability of CNN-based models, but this is hindered by the logistical challenges of sharing medical data. In this paper, we explore the feasibility of training our recently developed cloud-based segmentation tool (Histo-Cloud) using federated learning. Using a dataset of renal tissue biopsies we show that federated training to segment interstitial fibrosis and tubular atrophy (IFTA) using datasets from three institutions is not found to be different from a training by pooling the data on one server when tested on a fourth (holdout) institution’s data. Further, training a model to segment glomeruli for a federated dataset (split by staining) demonstrates similar performance.

Digital pathology using whole slide imaging (WSI) and artificial intelligence (AI) has the potential to transform diagnostic workflows, but adoption remains limited by technical complexity and scalability. We developed the Computational Renal Pathology Suite (ComPRePS), a scalable cloud-based platform that automates WSI ingestion, compartmental segmentation, feature extraction, and AI-assisted interpretation through an integrated high-performance architecture. ComPRePS was evaluated in two use cases. First, using 213 procurement biopsies, we compared conventional assessments with automated AI analyses and a hybrid AI-assisted expert workflow. ComPRePS AI-assisted methods achieved higher precision and significantly improved interobserver agreement for key lesions, including global glomerulosclerosis, interstitial fibrosis and tubular atrophy, and arterial intimal thickening. Second, ComPRePS enabled high-throughput quantitative profiling of glomerular and tubular features across minimal change disease, diabetic nephropathy, and amyloid nephropathy revealing disease-specific phenotypic patterns inaccessible to manual evaluation. Overall, ComPRePS improves reproducibility, interpretability, and objectivity in renal pathology, bridging computation with clinical practice.

The largest bottleneck to the development of convolutional neural network (CNN) models in the computational pathology domain is the collection and curation of diverse training datasets. Training CNNs requires large cohorts of image data, and model generalizability is dependent on training data heterogeneity. Including data from multiple centers enhances the generalizability of CNN based models, but this is hindered by the logistical challenges of sharing medical data. In this paper we explore the feasibility of training our recently developed cloud-based segmentation tool (Histo-Cloud) using federated learning. We show that a federated trained model to segment interstitial fibrosis and tubular atrophy (IFTA) using datasets from three institutions is comparable to a model trained by pooling the data on one server when tested on a fourth (holdout) institution's data. Further, training a model to segment glomeruli for a federated dataset (split by staining) demonstrates similar performance. Competing Interest Statement The authors have declared no competing interest.

Image-based machine learning tools hold great promise for clinical applications in nephropathology and kidney research. However, the ideal end-users of these computational tools (e.g., pathologists and biological scientists) often face prohibitive challenges in using these tools to their full potential, including the lack of technical expertise, suboptimal user interface, and limited computation power. We have developed Histo-Cloud, a tool for segmentation of whole slide images (WSIs) that has an easy-to-use graphical user interface. This tool runs a state-of-the-art convolutional neural network (CNN) for segmentation of WSIs in the cloud and allows the extraction of features from segmented regions for further analysis. By segmenting glomeruli, interstitial fibrosis and tubular atrophy, and vascular structures from renal and non-renal WSIs, we demonstrate the scalability, best practices for transfer learning, and effects of dataset variability. Finally, we demonstrate an application for animal model research, analyzing glomerular features in murine models of aging, diabetic nephropathy, and HIV associated nephropathy. The ability to access this tool over the internet will facilitate widespread use by computational non-experts. Histo-Cloud is open source and adaptable for segmentation of any histological structure regardless of stain. Histo-Cloud will greatly accelerate and facilitate the generation of datasets for machine learning in the analysis of kidney histology, empowering computationally novice end-users to conduct deep feature analysis of tissue slides. Competing Interest Statement J.E.Z. is a paid consultant for Leica Biosystems. Footnotes * Changed the Acknowledgements section. * https://github.com/SarderLab/Histo-cloud * https://hub.docker.com/r/sarderlab/histo-cloud * https://athena.ccr.buffalo.edu/ * https://bit.ly/3ejZhab * https://bit.ly/3nNMpfH * https://bit.ly/3r5GrZr