Effective Rapid Diagnosis of Bacterial and Fungal Bloodstream Infections by T2 Magnetic Resonance Technology in the Pediatric Population.

Children are prone to bloodstream infections (BSIs), the rapid and accurate diagnosis of which is an unmet clinical need. The T2MR technology is a direct molecular assay for identification of BSI pathogens, which can help to overcome the limits of blood culture (BC) such as diagnostic accuracy, blood volumes required, and turnaround time. We analyzed results obtained with the T2Bacteria (648) and T2Candida (106) panels in pediatric patients of the Bambino Gesù Children's Hospital between May 2018 and September 2020 in order to evaluate the performance of the T2Dx instrument with respect to BC. T2Bacteria and T2Candida panels showed 84.2% and 100% sensitivity with 85.9% and 94.1% specificity, respectively. The sensitivity and specificity of the T2Bacteria panel increased to 94.9% and 98.7%, respectively, when BC was negative but other laboratory data supported the molecular result. T2Bacteria sensitivity was 100% with blood volumes <2 mL in neonates and infants. T2Bacteria and T2Candida provided definitive microorganism identification in a mean time of 4.4 and 3.7 h, respectively, versus 65.7 and 125.5 h for BCs (P < 0.001). T2 panels rapidly and accurately enable a diagnosis of a pediatric BSI, even in children under 1 year of age and for very small blood volumes. These findings support their clinical use in life-threatening pediatric infections, where the time to diagnosis is of utmost importance, in order to improve survival and minimize the long-term sequalae of sepsis. The T2 technology could be further developed to include more bacteria and fungi species that are involved in the etiology of sepsis.

Artificial intelligence for differentiating COVID-19 from other viral pneumonias on CT: comparative analysis of different models based on quantitative and radiomic approaches.

BACKGROUND: To develop a pipeline for automatic extraction of quantitative metrics and radiomic features from lung computed tomography (CT) and develop artificial intelligence (AI) models supporting differential diagnosis between coronavirus disease 2019 (COVID-19) and other viral pneumonia (non-COVID-19). METHODS: Chest CT of 1,031 patients (811 for model building; 220 as independent validation set (IVS) with positive swab for severe acute respiratory syndrome coronavirus-2 (647 COVID-19) or other respiratory viruses (384 non-COVID-19) were segmented automatically. A Gaussian model, based on the HU histogram distribution describing well-aerated and ill portions, was optimised to calculate quantitative metrics (QM, n = 20) in both lungs (2L) and four geometrical subdivisions (GS) (upper front, lower front, upper dorsal, lower dorsal; n = 80). Radiomic features (RF) of first (RF1, n = 18) and second (RF2, n = 120) order were extracted from 2L using PyRadiomics tool. Extracted metrics were used to develop four multilayer-perceptron classifiers, built with different combinations of QM and RF: Model1 (RF1-2L); Model2 (QM-2L, QM-GS); Model3 (RF1-2L, RF2-2L); Model4 (RF1-2L, QM-2L, GS-2L, RF2-2L). RESULTS: The classifiers showed accuracy from 0.71 to 0.80 and area under the receiving operating characteristic curve (AUC) from 0.77 to 0.87 in differentiating COVID-19 versus non-COVID-19 pneumonia. Best results were associated with Model3 (AUC 0.867 ± 0.008) and Model4 (AUC 0.870 ± 0.011. For the IVS, the AUC values were 0.834 ± 0.008 for Model3 and 0.828 ± 0.011 for Model4. CONCLUSIONS: Four AI-based models for classifying patients as COVID-19 or non-COVID-19 viral pneumonia showed good diagnostic performances that could support clinical decisions.

Diagnostic Performance in Differentiating COVID-19 from Other Viral Pneumonias on CT Imaging: Multi-Reader Analysis Compared with an Artificial Intelligence-Based Model.

Growing evidence suggests that artificial intelligence tools could help radiologists in differentiating COVID-19 pneumonia from other types of viral (non-COVID-19) pneumonia. To test this hypothesis, an R-AI classifier capable of discriminating between COVID-19 and non-COVID-19 pneumonia was developed using CT chest scans of 1031 patients with positive swab for SARS-CoV-2 (n = 647) and other respiratory viruses (n = 384). The model was trained with 811 CT scans, while 220 CT scans (n = 151 COVID-19; n = 69 non-COVID-19) were used for independent validation. Four readers were enrolled to blindly evaluate the validation dataset using the CO-RADS score. A pandemic-like high suspicion scenario (CO-RADS 3 considered as COVID-19) and a low suspicion scenario (CO-RADS 3 considered as non-COVID-19) were simulated. Inter-reader agreement and performance metrics were calculated for human readers and R-AI classifier. The readers showed good agreement in assigning CO-RADS score (Gwet's AC2 = 0.71, p < 0.001). Considering human performance, accuracy = 78% and accuracy = 74% were obtained in the high and low suspicion scenarios, respectively, while the AI classifier achieved accuracy = 79% in distinguishing COVID-19 from non-COVID-19 pneumonia on the independent validation dataset. The R-AI classifier performance was equivalent or superior to human readers in all comparisons. Therefore, a R-AI classifier may support human readers in the difficult task of distinguishing COVID-19 from other types of viral pneumonia on CT imaging.

A human stem cell-based model for identifying adverse effects of organic and inorganic chemicals on the developing nervous system.

The aim of our study was to investigate whether a human neural stem cell line derived from umbilical cord blood (HUCB-NSC) can serve as a reliable test model for developmental neurotoxicity (DNT). We assessed the sensitivity of HUCB-NSCs at different developmental stages to a panel of neurotoxic (sodium tellurite, methylmercury chloride, cadmium chloride, chlorpyrifos, and L-glutamate) and non-neurotoxic (acetaminophen, theophylline, and D-glutamate) compounds. In addition, we investigated the effect of some compounds on key neurodevelopmental processes like cell proliferation, apoptotic cell death, and neuronal and glial differentiation. Less differentiated HUCB-NSCs were generally more sensitive to neurotoxicants, with the notable exception of L-glutamate, which showed a higher toxicity to later stages. The relative potencies of the compounds were: cadmium chloride > methylmercury chloride >> chlorpyrifos >> L-glutamate. Fifty nanomolar methylmercury chloride (MeHgCl) inhibited proliferation and induced apoptosis in early-stage cells. At the differentiated stage, 1 muM MeHgCl induced selective loss of S100 beta-expressing astrocytic cells. One millimolar L-glutamate did not influence the early stages of HUCB-NSC development, but it affected late stages of neuronal differentiation. A valuable system for in vitro DNT assessment should be able to discriminate between neurotoxic and non-neurotoxic compounds and show different susceptibilities to chemicals according to developmental stage and cell lineage. Although not exhaustive, this work shows that the HUCB-NSC model fulfils these criteria and may serve as a human in vitro model for DNT priority setting.

Advanced Glycation End Products (AGE) and Soluble Forms of AGE Receptor: Emerging Role as Mortality Risk Factors in CKD.

Advanced glycation end-products (AGE) can promote chronic kidney disease (CKD) progression and CKD-related morbidities. The soluble receptor for AGE (sRAGE) is a potential biomarker of inflammation and oxidative stress. Here, we explored the role of AGE, glycated albumin, sRAGE and its different forms, cRAGE and esRAGE, as prognostic factors for mortality in 111 advanced CKD patients. The median follow-up time was 39 months. AGE were quantified by fluorescence, sRAGE and its forms by ELISA. Malnutrition was screened by the Malnutrition Inflammation Score (MIS). The Cox proportional hazards regression model was used to assess the association of variables with all-cause mortality. Mean levels of sRAGE, esRAGE and cRAGE were 2318 ± 1224, 649 ± 454 and 1669 ± 901 pg/mL. The mean value of cRAGE/esRAGE was 2.82 ± 0.96. AGE were 3026 ± 766 AU and MIS 6.0 ± 4.7. eGFR correlated negatively with AGE, sRAGE, esRAGE and cRAGE, but not with cRAGE/esRAGE. Twenty-eight patients died. No difference was observed between diabetic and non-diabetic patients. Starting dialysis was not associated with enhanced risk of death. AGE, esRAGE and cRAGE/esRAGE were independently associated with all-cause mortality. AGE, esRAGE and cRAGE/esRAGE may help to stratify overall mortality risk. Implementing the clinical evaluation of CKD patients by quantifying these biomarkers can help to improve patient outcomes.

Redox homeostasis modulates the sensitivity of myeloma cells to bortezomib.

The use of proteasome inhibitors have been a major advance in the treatment of multiple myeloma (MM), but their mechanisms of action remain largely unclear. A better understanding of the cellular events downstream of proteasome inhibition is essential to improve the response and identify new combination therapies for MM and other malignancies. This study analysed the relationships between redox homeostasis and bortezomib treatment in MM cells. Our data showed that decreasing intracellular glutathione through buthionine sulfoximine treatment strongly enhances bortezomib toxicity, whilst antioxidants protect MM cells from bortezomib-mediated cell death. Bortezomib treatment decreases intracellular glutathione both in MM cell lines and in malignant plasma cells obtained from MM patients. Glutamate-cysteine ligase (GCLM) and haem-oxygenase-1 (HMOX1), two genes involved in the Nrf-2-mediated antioxidant response, as well as two eIF2alpha-downstream transcription factors, activating transcription factor 4 (ATF4) and C/EBP homologous protein (CHOP), are upregulated, indicating that redox-related adaptive responses are initiated in bortezomib-treated MM cells. These findings demonstrate tight links between sensitivity to proteasome inhibition and redox homeostasis in MM cells and have potential implications for treatment.

Dynamic retention of Ero1alpha and Ero1beta in the endoplasmic reticulum by interactions with PDI and ERp44.

Disulfide bonds are formed in the endoplasmic reticulum (ER) by sequential interchange reactions: Ero1alpha and Ero1beta transfer oxidative equivalents to protein disulfide isomerase (PDI), which in turn oxidizes cargo proteins. Neither Ero1alpha nor Ero1beta contains known ER localization motif(s), raising the question of how they are retained in this organelle. Here the authors show that, unlike endogenous molecules, overexpressed Ero1alpha and Ero1beta are secreted by HeLa transfectants, suggesting saturation of their normal retention mechanism(s). Co-expression of either PDI or ERp44 prevents Ero1 secretion in a KDEL/RDEL dependent way. Covalent interactions between ERp44 and Ero1 are essential for retention. In contrast, a mutant PDI lacking the four cysteines in the two active sites still inhibits secretion, albeit less efficiently. PDI and ERp44 compete for Ero1 binding. PDI also prevents Ero1 aggregation and dimerization, thus chaperoning its own oxidase. This dynamic retention mechanism of Ero1 may be important for fine-tuning the regulation of ER redox homeostasis and quality control.