Search

Objectives Patients and methods Results Conclusion To evaluate a cancer detecting artificial intelligence (AI) algorithm on serial biopsies in patients with prostate cancer on active surveillance (AS).A total of 180 patients in the Prostate Cancer Research International Active Surveillance (PRIAS) cohort were prospectively monitored using pre‐defined criteria. Diagnostic and re‐biopsy slides from 2011 to 2020 (n = 4744) were scanned and analysed by an in‐house AI‐based cancer detection algorithm. The algorithm was analysed for sensitivity, specificity, and for accuracy to predict need for active treatment. Prognostic properties of cancer size, prostate‐specific antigen (PSA) level and PSA density at diagnosis were evaluated.The sensitivity and specificity of the AI algorithm was 0.96 and 0.73, respectively, for correct detection of cancer areas. Original pathology report diagnosis was used as the reference method. The area of cancer estimated by the pathologists correlated highly with the AI detected cancer size (r = 0.83). By using the AI algorithm, 63% of the slides would not need to be read by a pathologist as they were classed as benign, at the risk of missing 0.55% slides containing cancer. Biopsy cancer content and PSA density at diagnosis were found to be prognostic of whether the patient stayed on AS or was discontinued for active treatment.The AI‐based biopsy cancer detection algorithm could be used to reduce the pathologists’ workload in an AS cohort. The detected cancer amount correlated well with the cancer length measured by the pathologist and the algorithm performed well in finding even small areas of cancer. To our knowledge, this is the first report on an AI‐based algorithm in digital pathology used to detect cancer in a cohort of patients on AS. [ABSTRACT FROM AUTHOR]

Background: CSF biomarkers are key AD descriptors, but the CSF volume as well as the production and clearance rates of proteins varies in individuals. Adjusting for reference proteins, which reflect these factors, might improve the accuracy of CSF biomarkers, similar to when normalizing CSF Aβ42 levels to Aβ40 levels. For other biomarkers, individual CSF dynamics are seldom adjusted for, which may result in disagreement with other techniques (e.g., PET). Additionally, this may contribute to inaccurate conclusions about CSF protein correlations that mainly exist due to high or low CSF dilution levels. The aim of this work is therefore to establish the use of CSF reference proteins to improve CSF AD biomarker accuracy. Method: We used measurements of 2944 CSF proteins in the Swedish BioFINDER‐2 cohort (n = 982, 80% train and 20% test). Reference protein candidates (reflecting CSF dilution levels without being related to any disease) were searched for in a data‐driven manner, using t‐distributed stochastic neighbor embedding (t‐SNE) and a semi‐supervised K‐means clustering algorithm. The final candidate proteins were evaluated in logistic regression models, using CSF P‐tau181 and CSF Aβ42 as main predictors of tau‐PET and amyloid‐PET, respectively. Result: Most proteins were associated with an individual dilution level, although at different strengths (Fig 1 and Fig 2e). From the different colorings of the t‐SNE space, proteins of similar expressions were placed nearby, creating areas of extra interest (yellow areas in subfigures 2c‐2h). The semi‐supervised K‐means algorithm highlighted an area (cluster 11) containing proteins with superior reference protein characteristics, from which single reference protein candidates were identified. All examined references (e.g., Aβ40, NTRK3, NTRK2, BLMH,CBLN4, PTPRN2 or PTPRS) outperformed using no reference for both the P‐tau181 and Aβ42 model (Figure 3). For P‐tau181, highest performance was achieved using CBLN4 (AUC = 0.944). For Aβ42, Aβ40 performed best as reference (AUC = 0.992). Conclusion: The individual CSF dilution level affects many CSF proteins. We describe several novel and previously suggested CSF reference proteins, which can significantly improve the accuracy of CSF biomarkers as key AD descriptors. [ABSTRACT FROM AUTHOR]

Haemolytic uraemic syndrome (HUS) is defined by the simultaneous occurrence of nonimmune haemolytic anaemia, thrombocytopenia and acute renal failure. This leads to the pathological lesion termed thrombotic microangiopathy, which mainly affects the kidney, as well as other organs. HUS is associated with endothelial cell injury and platelet activation, although the underlying cause may differ. Most cases of HUS are associated with gastrointestinal infection with Shiga toxin-producing enterohaemorrhagic Escherichia coli (EHEC) strains. Atypical HUS (aHUS) is associated with complement dysregulation due to mutations or autoantibodies. In this review, we will describe the causes of HUS. In addition, we will review the clinical, pathological, haematological and biochemical features, epidemiology and pathogenetic mechanisms as well as the biochemical, microbiological, immunological and genetic investigations leading to diagnosis. Understanding the underlying mechanisms of the different subtypes of HUS enables tailoring of appropriate treatment and management. To date, there is no specific treatment for EHEC-associated HUS but patients benefit from supportive care, whereas patients with aHUS are effectively treated with anti-C5 antibody to prevent recurrences, both before and after renal transplantation. [ABSTRACT FROM AUTHOR]