Verifying Properties of Large Sets of Processes with Network Invariants

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Isolated pelvic bone involvement as a presentation of alveolar echinococcosis.

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Genetic diversity of Echinococcus multilocularis specimens isolated from Belgian patients with alveolar echinococcosis using EmsB microsatellites analysis.

The genetic diversity of Echinococcus multilocularis (E. multilocularis) specimens isolated from patients with alveolar echinococcosis (AE), is a major field of investigation to correlate with sources of infection, clinical manifestations and prognosis of the disease. Molecular markers able to distinguish samples are commonly used worldwide, including the EmsB microsatellite. Here, we report the use of the EmsB microsatellite polymorphism data mining for the retrospective typing of Belgian specimens of E. multilocularis infecting humans. A total of 18 samples from 16 AE patients treated between 2006 and 2021 were analyzed through the EmsB polymorphism. Classification of specimens was performed through a dendrogram construction in order to compare the similarity among Belgian samples, some human referenced specimens on the EWET database (EmsB Website for the Echinococcus Typing) and previously published EmsB profiles from red foxes circulating in/near Belgium. According to a comparison with human European specimens previously genotyped in profiles, the 18 Belgian ones were classified into three EmsB profiles. Four specimens could not be assigned to an already known profile but some are near to EWET referenced samples. This study also highlights that some specimens share the same EmsB profile with profiles characterized in red foxes from north Belgium, the Netherlands, Luxembourg and French department near to the Belgian border. Furthermore, Belgian specimens present a genetic diversity and include one profile that don't share similarities with the ones referenced in the EWET database. However, at this geographical scale, there is no clear correlation between EmsB profiles and geographical location. Further studies including additional clinical samples and isolates from foxes and rodents of south Belgium are necessary to better understand the spatial and temporal circumstances of human infections but also a potential correlation between EmsB profiles and parasite virulence

Myoferlin controls mitochondrial structure and activity in pancreatic ductal adenocarcinoma, and affects tumor aggressiveness

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related death. Therapeutic options remain very limited and are based on classical chemotherapies. Energy metabolism reprogramming appears as an emerging hallmark of cancer and is considered a therapeutic target with considerable potential. Myoferlin, a ferlin family member protein overexpressed in PDAC, is involved in plasma membrane biology and has a tumor-promoting function. In the continuity of our previous studies, we investigated the role of myoferlin in the context of energy metabolism in PDAC. We used selected PDAC tumor samples and PDAC cell lines together with small interfering RNA technology to study the role of myoferlin in energetic metabolism. In PDAC patients, we showed that myoferlin expression is negatively correlated with overall survival and with glycolytic activity evaluated by 18F-deoxyglucose positron emission tomography. We found out that myoferlin is more abundant in lipogenic pancreatic cancer cell lines and is required to maintain a branched mitochondrial structure and a high oxidative phosphorylation activity. The observed mitochondrial fission induced by myoferlin depletion led to a decrease of cell proliferation, ATP production, and autophagy induction, thus indicating an essential role of myoferlin for PDAC cell fitness. The metabolic phenotype switch generated by myoferlin silencing could open up a new perspective in the development of therapeutic strategies, especially in the context of energy metabolism

2022-RA-193-ESGO Cost-effectiveness of molecular profiling for endometrial neoplasia: a single institution experience

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An externally validated fully automated deep learning algorithm to classify COVID-19 and other pneumonias on chest computed tomography.

peer reviewedPurpose: In this study, we propose an artificial intelligence (AI) framework based on three-dimensional convolutional neural networks to classify computed tomography (CT) scans of patients with coronavirus disease 2019 (COVID-19), influenza/community-acquired pneumonia (CAP), and no infection, after automatic segmentation of the lungs and lung abnormalities. Methods: The AI classification model is based on inflated three-dimensional Inception architecture and was trained and validated on retrospective data of CT images of 667 adult patients (no infection n=188, COVID-19 n=230, influenza/CAP n=249) and 210 adult patients (no infection n=70, COVID-19 n=70, influenza/CAP n=70), respectively. The model's performance was independently evaluated on an internal test set of 273 adult patients (no infection n=55, COVID-19 n= 94, influenza/CAP n=124) and an external validation set from a different centre (305 adult patients: COVID-19 n=169, no infection n=76, influenza/CAP n=60). Results: The model showed excellent performance in the external validation set with area under the curve of 0.90, 0.92 and 0.92 for COVID-19, influenza/CAP and no infection, respectively. The selection of the input slices based on automatic segmentation of the abnormalities in the lung reduces analysis time (56 s per scan) and computational burden of the model. The Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) score of the proposed model is 47% (15 out of 32 TRIPOD items). Conclusion: This AI solution provides rapid and accurate diagnosis in patients suspected of COVID-19 infection and influenza

Can predicting COVID-19 mortality in a European cohort using only demographic and comorbidity data surpass age-based prediction: An externally validated study.

peer reviewedOBJECTIVE: To establish whether one can build a mortality prediction model for COVID-19 patients based solely on demographics and comorbidity data that outperforms age alone. Such a model could be a precursor to implementing smart lockdowns and vaccine distribution strategies. METHODS: The training cohort comprised 2337 COVID-19 inpatients from nine hospitals in The Netherlands. The clinical outcome was death within 21 days of being discharged. The features were derived from electronic health records collected during admission. Three feature selection methods were used: LASSO, univariate using a novel metric, and pairwise (age being half of each pair). 478 patients from Belgium were used to test the model. All modeling attempts were compared against an age-only model. RESULTS: In the training cohort, the mortality group's median age was 77 years (interquartile range = 70-83), higher than the non-mortality group (median = 65, IQR = 55-75). The incidence of former/active smokers, male gender, hypertension, diabetes, dementia, cancer, chronic obstructive pulmonary disease, chronic cardiac disease, chronic neurological disease, and chronic kidney disease was higher in the mortality group. All stated differences were statistically significant after Bonferroni correction. LASSO selected eight features, novel univariate chose five, and pairwise chose none. No model was able to surpass an age-only model in the external validation set, where age had an AUC of 0.85 and a balanced accuracy of 0.77. CONCLUSION: When applied to an external validation set, we found that an age-only mortality model outperformed all modeling attempts (curated on www.covid19risk.ai) using three feature selection methods on 22 demographic and comorbid features

The uptake of 18F-FDG by renal allograft in kidney transplant recipients is not influenced by renal function.

PURPOSE OF THE REPORT: F-FDG PET/CT has been recently proposed as a noninvasive tool for the diagnosis of renal allograft acute rejection (AR) in kidney transplant recipients (KTRs). Still, the influence of kidney function on F-FDG uptake by renal grafts remains unknown. PATIENTS AND METHODS: We retrospectively identified all KTRs who underwent at least one F-FDG PET/CT. Kidney transplant recipients with documented pyelonephritis or AR were excluded. Estimated glomerular filtration rate (eGFR) was assessed using chronic kidney disease (CKD)-EPI equation. Mean standardized uptake values (SUVmean) of renal graft cortex and aorta were measured in 4 and 1 volumes of interest, respectively. Spearman rank correlation coefficient (rho) and analysis of variance (ANOVA) were performed. RESULTS: Eighty-two KTRs underwent F-FDG PET/CT for tumor staging (n = 46), suspected infection (n = 11), or fever of unknown origin (n = 25). Mean eGFR was 50 +/- 19 mL/min per 1.73 m, including CKD stage 1 (n = 3), stage 2 (n = 21), stage 3a (n = 20), stage 3b (n = 29), and stage 4 (n = 9). Mean kidney and aorta SUVmean were 1.8 +/- 0.2 and 1.7 +/- 0.3, respectively. No significant correlation was observed between eGFR and kidney SUVmean (rho, 0.119; P, 0.28) or aorta SUVmean (rho, -0.144; P, 0.20). ANOVA showed no difference of kidney (P, 0.62) and aorta (P, 0.85) SUVmean between CKD groups. Mean coefficient of variation (on the basis of kidney SUVmean of >3 consecutive F-FDG PET/CT in 15 patients with no significant change of eGFR) reached 13.1%. CONCLUSIONS: The uptake of F-FDG by renal allografts within an hour postinjection is not significantly impacted by CKD