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Background: Alzheimer's disease (AD) is a neurodegenerative condition for which there is currently no available medication that can stop its progression. Previous studies suggest that mild cognitive impairment (MCI) is a phase that precedes the disease. Therefore, a better understanding of the molecular mechanisms behind MCI conversion to AD is needed. Method: Here, we propose a machine learning-based approach to detect the key metabolites and proteins involved in MCI progression to AD using data from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery Study. Proteins and metabolites were evaluated separately in multiclass models (controls, MCI and AD) and together in MCI conversion models (MCI stable vs converter). Only features selected as relevant by 3/4 algorithms proposed were kept for downstream analysis. Results: Multiclass models of metabolites highlighted nine features further validated in an independent cohort (0.726 mean balanced accuracy). Among these features, one metabolite, oleamide, was selected by all the algorithms. Further in-vitro experiments in rodents showed that disease-associated microglia excreted oleamide in vesicles. Multiclass models of proteins stood out with nine features, validated in an independent cohort (0.720 mean balanced accuracy). However, none of the proteins was selected by all the algorithms. Besides, to distinguish between MCI stable and converters, 14 key features were selected (0.872 AUC), including tTau, alpha-synuclein (SNCA), junctophilin-3 (JPH3), properdin (CFP) and peptidase inhibitor 15 (PI15) among others. Conclusions: This omics integration approach highlighted a set of molecules associated with MCI conversion important in neuronal and glia inflammation pathways.

Contains fulltext : 305495.pdf (Publisher’s version ) (Open Access), Programmed death-ligand 1 (PD-L1) expression is currently used in the clinic to assess eligibility for immune-checkpoint inhibitors via the tumor proportion score (TPS), but its efficacy is limited by high interobserver variability. Multiple papers have presented systems for the automatic quantification of TPS, but none report on the task of determining cell-level PD-L1 expression and often reserve their evaluation to a single PD-L1 monoclonal antibody or clinical center. In this paper, we report on a deep learning algorithm for detecting PD-L1 negative and positive tumor cells at a cellular level and evaluate it on a cell-level reference standard established by six readers on a multi-centric, multi PD-L1 assay dataset. This reference standard also provides for the first time a benchmark for computer vision algorithms. In addition, in line with other papers, we also evaluate our algorithm at slide-level by measuring the agreement between the algorithm and six pathologists on TPS quantification. We find a moderately low interobserver agreement at cell-level level (mean reader-reader F1 score = 0.68) which our algorithm sits slightly under (mean reader-AI F1 score = 0.55), especially for cases from the clinical center not included in the training set. Despite this, we find good AI-pathologist agreement on quantifying TPS compared to the interobserver agreement (mean reader-reader Cohen's kappa = 0.54, 95% CI 0.26-0.81, mean reader-AI kappa = 0.49, 95% CI 0.27-0.72). In conclusion, our deep learning algorithm demonstrates promise in detecting PD-L1 expression at a cellular level and exhibits favorable agreement with pathologists in quantifying the tumor proportion score (TPS). We publicly release our models for use via the Grand-Challenge platform.

Item does not contain fulltext, Immune checkpoint blockade has become standard treatment for many types of cancer. Such therapy is indicated most often in patients with advanced or metastatic disease but has been increasingly used as adjuvant therapy in those with early-stage disease. Adverse events include immune-related organ inflammation resembling autoimmune diseases. We describe a case of severe immune-related gastroenterocolitis in a 4-month-old infant who presented with intractable diarrhea and failure to thrive after in utero exposure to pembrolizumab. Known causes of the symptoms were ruled out, and the diagnosis of pembrolizumab-induced immune-related gastroenterocolitis was supported by the results of histopathological assays, immunophenotyping, and analysis of the level of antibodies against programmed cell death protein 1 (PD-1). The infant's condition was successfully treated with prednisolone and infliximab.

Contains fulltext : 300043.pdf (Publisher’s version ) (Open Access), BACKGROUND: No validated and standardized animal models of pulmonary air leakage (PAL) exist for testing aerostatic efficacy of lung sealants. Lack of negative control groups in published studies and intrinsic sealing mechanisms of healthy animal lungs might contribute to a translational gap, leading to poor clinical results. This study aims to address the impact of intrinsic sealing mechanisms on the validity of PAL models, and investigate the conditions required for an ovine model of PAL for lung sealant testing. METHODS: An ovine acute aerostasis model was developed, consisting of a bilateral thoracotomy with lesion creation, chest tube insertion and monitoring of air leaks using digital drains (≥80 minutes), under spontaneous respiration. Healthy mixed-breed adult female sheep were used and all in vivo procedures were performed under terminal anesthesia. Superficial parenchymal lesions were tested post-mortem and in vivo, extended lesions including bronchioles (deep bowl-shaped and sequential lung amputation lesions) were tested in vivo. Experiment outcomes include air leakage (AL), minimal leaking pressure (MLP) and histology. RESULTS: Two post-mortem (N=4 superficial parenchymal lesions) and 10 in vivo experiments (N=5 superficial parenchymal and N=16 lesions involving bronchioles) were performed. In contrast to the post-mortem model, superficial parenchymal lesions in vivo showed less air leak [mean flow ± standard deviation (SD): 760±693 vs. 42±33 mL/min, P=0.055]. All superficial parenchymal lesions in vivo sealed intrinsically within a median time of 20 minutes [interquartile range (IQR), 10-75 minutes]. Histology of the intrinsic sealing layer revealed an extended area of alveolar collapse below the incision with intra-alveolar hemorrhage. Compared to superficial parenchymal lesions in vivo, lesions involving bronchioles induced significantly higher air leak post-operatively (normalized mean flow ± SD: 459±221 mL/min, P=0.003). At termination, 5/9 (55.6%)

Preoperative clinical MRI protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this second part, we review magnetic resonance spectroscopy (MRS), chemical exchange saturation transfer (CEST), susceptibility-weighted imaging (SWI), MRI-PET, MR elastography (MRE), and MR-based radiomics applications. The first part of this review addresses dynamic susceptibility contrast (DSC) and dynamic contrast-enhanced (DCE) MRI, arterial spin labeling (ASL), diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting (MRF).

Preoperative clinical MRI protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation or lack thereof. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this first part, we discuss dynamic susceptibility contrast (DSC) and dynamic contrast-enhanced (DCE) MRI, arterial spin labeling (ASL), diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting (MRF). The second part of this review addresses magnetic resonance spectroscopy (MRS), chemical exchange saturation transfer (CEST), susceptibility-weighted imaging (SWI), MRI-PET, MR elastography (MRE), and MR-based radiomics applications.

High-risk large B-cell lymphoma (LBCL) has poor outcomes with standard first-line chemoimmunotherapy. In the phase 2, multicenter, single-arm ZUMA-12 study (ClinicalTrials.gov NCT03761056) we evaluated axicabtagene ciloleucel (axi-cel), an autologous anti-CD19 chimeric antigen receptor (CAR) T-cell therapy, as part of first-line treatment in 40 patients with high-risk LBCL. This trial has completed accrual. The primary outcome was complete response rate (CRR). Secondary outcomes were objective response rate (ORR), duration of response (DOR), event-free survival (EFS), progression-free survival (PFS), overall survival (OS), assessment of safety, central nervous system (CNS) relapse and blood levels of CAR T cells and cytokines. The primary endpoint in efficacy-evaluable patients (n = 37) was met, with 78% CRR (95% confidence interval (CI), 62-90) and 89% ORR (95% CI, 75-97). As of 17 May 2021 (median follow-up, 15.9 months), 73% of patients remained in objective response; median DOR, EFS and PFS were not reached. Grade ≥3 cytokine release syndrome (CRS) and neurologic events occurred in three patients (8%) and nine patients (23%), respectively. There were no treatment-related grade 5 events. Robust CAR T-cell expansion occurred in all patients with a median time to peak of 8 days. We conclude that axi-cel is highly effective as part of first-line therapy for high-risk LBCL, with a manageable safety profile.

Item does not contain fulltext, Recent progress in the development of artificial intelligence (AI) has sparked enthusiasm for its potential use in pathology. As pathology labs are currently starting to shift their focus towards AI implementation, a better understanding how AI tools can be optimally aligned with the medical and social context of pathology daily practice is urgently needed. Strikingly, studies often fail to mention the ways in which AI tools should be integrated in the decision-making processes of pathologists, nor do they address how this can be achieved in an ethically sound way. Moreover, the perspectives of pathologists and other professionals within pathology concerning the integration of AI within pathology remains an underreported topic. This article aims to fill this gap in the literature and presents the first in-depth interview study in which professionals' perspectives on the possibilities, conditions and prerequisites of AI integration in pathology are explicated. The results of this study have led to the formulation of three concrete recommendations to support AI integration, namely: (1) foster a pragmatic attitude toward AI development, (2) provide task-sensitive information and training to health care professionals working in pathology departments and (3) take time to reflect upon users' changing roles and responsibilities.

01 december 2022, Item does not contain fulltext, BACKGROUND: The impact of serrated polyps on the advanced colorectal neoplasia (CRN) risk in inflammatory bowel disease (IBD) patients is unknown. Serrated polyps are histologically categorized as hyperplastic polyps (HPs), sessile serrated lesions (SSLs), and traditional serrated adenomas (TSAs). AIMS: We aimed (1) to characterize the serrated polyps in IBD patients, (2) to identify factors associated with the presence of serrated polyps in IBD, and (3) to assess the CRN risk in IBD patients with serrated polyps. METHODS: We established a retrospective cohort of IBD patients with and without colonic serrated polyps. Cox-regression analysis with time-dependent variables was used to compare advanced CRN risk in IBD patients with and without serrated polyps. RESULTS: Of the 621 enrolled IBD patients, 198 had a serrated polyp (92 HPs, 88 SSLs without dysplasia, 13 SSLs with dysplasia, and 5 TSAs). Independent factors associated with serrated polyps were ulcerative colitis (UC) (odds ratio (OR) 1.77, 95% confidence interval (CI) 1.19-2.62, p = 0.005), male gender (OR 1.63, 95% CI 1.11-2.40, p = 0.013), and older age (per year increase, OR 1.06, 95%CI 1.05-1.08, p < 0.001). TSAs and SSLs with dysplasia were risk factors for subsequent advanced CRN (HR 13.51, 95% CI 3.11-58.68, p < 0.001), while HPs (HR 1.98, 95% CI 0.46-8.60, p = 0.36) and SSLs without dysplasia (HR 0.87, 95% CI 0.11-6.88, p-0.89) did not impact the subsequent advanced CRN risk. CONCLUSIONS: UC, male gender and older age were associated with the presence of serrated polyps. The majority of serrated polyps (91%) were HPs and SSL without dysplasia and did not affect the CRC risk. However TSAs and SSLs with dysplasia, representing a small subgroup of serrated polyps (9%), were associated with subsequent advanced CRN.

Image-derived phenotypes (IDPs) from multimodal MRI sequences constitute an important resource that allows the characterization of brain alterations in the early stages of Alzheimer diseases and other neurodegenerative conditions. Here, we showed the computation of multimodal IDPs from the European Prevention of Alzheimer Dementia (EPAD) cohort and assessed their relationship with non-imaging markers of neurodegeneration. We demonstrated the clinical relevance of IDPs to uncover early brain alteration in AD by showing expected association with non-imaging data.

Arterial spin labeling (ASL) is a non-invasive MRI technique, allowing quantitative measurement of cerebral perfusion. Incomplete or inaccurate reporting of acquisition parameters complicates quantification, analysis, and sharing of ASL data, particularly for studies across multiple sites, platforms, and ASL methods. Therefore, there is a strong need for standardization of ASL data storage, including acquisition metadata. Recently ASL-BIDS, the BIDS extension for ASL, was developed and released in BIDS 1.5.0. This manuscript provides an overview of the development and design choices of this first ASL-BIDS extension, which is mainly aimed at clinical ASL applications. The structure of the ASL data, focusing on storage order of the ASL time series and implementation of calibration approaches, unit scaling, ASL-related BIDS fields, and storage of the labeling plane information, are discussed. Additionally, an overview of ASL-BIDS compatible conversion and ASL analysis software and ASL example datasets in BIDS format is provided. It is anticipated that large-scale adoption of ASL-BIDS will improve the reproducibility of ASL research.

The European Prevention of Alzheimer Dementia (EPAD) is a multi-center study that aims to characterize the preclinical and prodromal stages of Alzheimer’s Disease. The EPAD imaging dataset includes core (3D T1w, 3D FLAIR) and advanced (ASL, diffusion MRI, and resting-state fMRI) MRI sequences. Here, we give an overview of the semi-automatic multimodal and multisite pipeline that we developed to curate, preprocess, quality control (QC), and compute image-derived phenotypes (IDPs) from the EPAD MRI dataset. This pipeline harmonizes DICOM data structure across sites and performs standardized MRI pre- processing steps. A semi-automated MRI QC procedure was implemented to visualize and flag MRI images next to site-specific distributions of QC features — i.e. metrics that represent image quality. The value of each of these QC features was evaluated through comparison with visual assessment and step-wise parameter selection based on logistic regression. IDPs were computed from 5 different MRI modalities and their sanity and potential clinical relevance were ascertained by assessing their relationship with biological markers of aging and dementia. The EPAD v1500.0 data release encompassed core structural scans from 1356 participants 842 fMRI, 831 dMRI, and 858 ASL scans. From 1356 3D T1w images, we identified 17 images with poor quality and 61 with moderate quality. Five QC features — Signal to Noise Ratio (SNR), Contrast to Noise Ratio (CNR), Coefficient of Joint Variation (CJV), Foreground-Background energy Ratio (FBER), and Image Quality Rate (IQR) — were selected as the most informative on image quality by comparison with visual assessment. The multimodal IDPs showed greater impairment in associations with age and dementia biomarkers, demonstrating the potential of the dataset for future clinical analyses.

AIMS: The aim of this systematic review is to assess the effects of community pharmacist-led interventions to optimise the use of antibiotics and identify which interventions are most effective. METHODS: This review was conducted according to the PRISMA guidelines (PROSPERO: CRD42020188552). PubMed, EMBASE and the Cochrane Central Register of Controlled Trials were searched for (randomised) controlled trials. Included interventions were required to target antibiotic use, be set in the community pharmacy context, and be pharmacist-led. Primary outcomes were quality of antibiotic supply and adverse effects while secondary outcomes included patient-reported outcomes. Risk of bias was assessed using the 'Cochrane suggested risk of bias criteria' and narrative synthesis of primary outcomes conducted. RESULTS: Seventeen studies were included covering in total 3822 patients (mean age 45.6 years, 61.9% female). Most studies used educational interventions. Three studies reported on primary outcomes, 12 on secondary outcomes and two on both. Three studies reported improvements in quality of dispensing, interventions led to more intensive symptom assessment (up to 30% more advice given) and a reduction of over-the-counter supply up to 53%. Three studies led to higher consumer satisfaction, effects on adherence from nine studies were mixed (risk difference 0.04 [-0.02, 0.10]). All studies had unclear or high risks of bias across at least one domain, with large heterogeneity between studies. CONCLUSIONS: Our review suggests some positive results from pharmacist-led interventions, but the interventions do not seem sufficiently effective as currently implemented. This review should be interpreted as exploratory research, as more high-quality research is needed.

This study goes beyond the classic paradigm that newly qualified teachers (NQTs) are in need of formal support in order to cope with the challenges they face during the early stages of their teaching career. The focus of this survey study with 443 NQTs in Flanders, Belgium is whether NSTs' proactive seeking of feedback from the principal and from their colleagues acts as a mediator between the characteristics of their collegial network and NQTs' job satisfaction. The results reveal that seeking feedback from the principal and colleagues mediates the relation between knowing other colleagues' areas of expertise and valuing that expertise in the collegial network, on the one hand, and job satisfaction, on the other. More specifically, seeking feedback from the principal was more strongly related to NQTs' job satisfaction than seeking feedback from colleagues. Remarkably, psychological safety was only related to seeking feedback from colleagues. No relationship was found between psychological safety and seeking feedback from the principal. The study concludes with suggestions on how to support the professional development of NQTs.