A cationic tetrapyrrole inhibits toxic activities of the cellular prion protein

Prion diseases are rare neurodegenerative conditions associated with the conformational conversion of the cellular prion protein (PrPC) into PrPSc, a self-replicating isoform (prion) that accumulates in the central nervous system of affected individuals. The structure of PrPSc is poorly defined, and likely to be heterogeneous, as suggested by the existence of different prion strains. The latter represents a relevant problem for therapy in prion diseases, as some potent anti-prion compounds have shown strain-specificity. Designing therapeutics that target PrPC may provide an opportunity to overcome these problems. PrPC ligands may theoretically inhibit the replication of multiple prion strains, by acting on the common substrate of any prion replication reaction. Here, we characterized the properties of a cationic tetrapyrrole [Fe(III)-TMPyP], which was previously shown to bind PrPC, and inhibit the replication of a mouse prion strain. We report that the compound is active against multiple prion strains in vitro and in cells. Interestingly, we also find that Fe(III)-TMPyP inhibits several PrPC-related toxic activities, including the channel-forming ability of a PrP mutant, and the PrPC-dependent synaptotoxicity of amyloid-beta (A beta) oligomers, which are associated with Alzheimer's Disease. These results demonstrate that molecules binding to PrPC may produce a dual effect of blocking prion replication and inhibiting PrPC-mediated toxicity

DECLINE OF PREVALENCE OF RESISTANCE ASSOCIATED SUBSTITUTIONS TO NS3 AND NS5A INHIBITORS AT DAA- FAILURE IN HEPATITIS C VIRUS IN ITALY OVER THE YEARS 2015 TO 2018

Background: A minority of patients fails to eliminate HCV and resistance-associated substitutions (RASs) are commonly detected at failure of interferon-free DAA regimens . Methods: Within the Italian network VIRONET-C, the prevalence of NS3/NS5A/NS5B RASs was retrospectively evaluated in patients who failed an EASL recommended DAA-regimen in 2015-2018 . The geno2pheno system and Sorbo MC et al. Drug Resistance Updates 2018 were used to infer HCV- genotype/subtype and predict drug resistance . The changes in prevalence of RASs over time were evaluated by chi-square test for trend, predictors of RASs at failure were analysed by logistic regression . Results: We included 386 HCV infected patients: 75% males, median age was 56 years (IQR 52-61), metavir fibrosis stage F4 in 76%; 106 (28%) were treatment- experienced: 91 (86%) with IFN-based treatments, 26 (25%) with DAAs. Patients with HIV and HBV coinfection were 10% (33/317) and 8% (6/72), respectively. HCV genotype was 1b in 122 pts (32%), 3 in 109 (28%), 1a in 97 (25%), 4 in 37 (10%), 2 in 21 (5%). DAA regimens were: LDV/SOF in 115 (30%), DCV/SOF in 103 (27%), 3D in 83 (21%), EBR/GRZ in 32 (8%), VEL/SOF in 29 (7%), GLE/PIB in 18 (5%) and 2D in 6 (2%); ribavirin was administered in 123 (32%) . The NS5A fasta-sequence was available for all patients, NS5B for 361 (94%), NS3 for 365 (95%) . According to the DAA failed the prevalence of any RASs was 90%, namely 80/135 (59%) in NS3, 313/359 (87%) in NS5A, 114/286 (40%) in NS5B . The prevalence of any RASs significantly declined from 2015 to 2018 (93% vs 70%, p=0.004): NS5A RASs from 90% to 72% (p=0 .29), NS3 RASs from 74% to 18% (p<0 .001), while NS5B RASs remained stable . Independent predictors of any RASs included advanced fibrosis (AOR 6.1, CI 95% 1.8-20.3, p=0 .004) and genotype (G2 vs G1a AOR 0 .03, CI 95% 0 .002- 0 .31, p=0 .004; G3 vs G1a AOR 0 .08, CI 95% 0 .01-0 .62, p=0 .02; G4 vs G1a AOR 0 .05, CI 95% 0 .006-0 .46, p=0 .008), after adjusting for age, previous HCV treatment and year of genotype . Notably, full activity was predicted for GLE/PIB in 75% of cases and for at least two components of VEL/SOF/VOX in 53% of cases, no case with full-resistance to either regimen was found . Conclusion: Despite decreasing prevalence over the years, RASs remain common at virological failure of DAA treatment, particularly in patients with the highest grade of liver fibrosis. The identification of RASs after failure could play a crucial role in optimizing retreatment strategies

A Two-Step Feature Selection Radiomic Approach to Predict Molecular Outcomes in Breast Cancer

Breast Cancer (BC) is the most common cancer among women worldwide and is characterized by intra- and inter-tumor heterogeneity that strongly contributes towards its poor prognosis. The Estrogen Receptor (ER), Progesterone Receptor (PR), Human Epidermal Growth Factor Receptor 2 (HER2), and Ki67 antigen are the most examined markers depicting BC heterogeneity and have been shown to have a strong impact on BC prognosis. Radiomics can noninvasively predict BC heterogeneity through the quantitative evaluation of medical images, such as Magnetic Resonance Imaging (MRI), which has become increasingly important in the detection and characterization of BC. However, the lack of comprehensive BC datasets in terms of molecular outcomes and MRI modalities, and the absence of a general methodology to build and compare feature selection approaches and predictive models, limit the routine use of radiomics in the BC clinical practice. In this work, a new radiomic approach based on a two-step feature selection process was proposed to build predictors for ER, PR, HER2, and Ki67 markers. An in-house dataset was used, containing 92 multiparametric MRIs of patients with histologically proven BC and all four relevant biomarkers available. Thousands of radiomic features were extracted from post-contrast and subtracted Dynamic Contrast-Enanched (DCE) MRI images, Apparent Diffusion Coefficient (ADC) maps, and T2-weighted (T2) images. The two-step feature selection approach was used to identify significant radiomic features properly and then to build the final prediction models. They showed remarkable results in terms of F1-score for all the biomarkers: 84%, 63%, 90%, and 72% for ER, HER2, Ki67, and PR, respectively. When possible, the models were validated on the TCGA/TCIA Breast Cancer dataset, returning promising results (F1-score = 88% for the ER+/ER− classification task). The developed approach efficiently characterized BC heterogeneity according to the examined molecular biomarkers

A Two-Step Feature Selection Radiomic Approach to Predict Molecular Outcomes in Breast Cancer

Breast Cancer (BC) is the most common cancer among women worldwide and is characterized by intra- and inter-tumor heterogeneity that strongly contributes towards its poor prognosis. The Estrogen Receptor (ER), Progesterone Receptor (PR), Human Epidermal Growth Factor Receptor 2 (HER2), and Ki67 antigen are the most examined markers depicting BC heterogeneity and have been shown to have a strong impact on BC prognosis. Radiomics can noninvasively predict BC heterogeneity through the quantitative evaluation of medical images, such as Magnetic Resonance Imaging (MRI), which has become increasingly important in the detection and characterization of BC. However, the lack of comprehensive BC datasets in terms of molecular outcomes and MRI modalities, and the absence of a general methodology to build and compare feature selection approaches and predictive models, limit the routine use of radiomics in the BC clinical practice. In this work, a new radiomic approach based on a two-step feature selection process was proposed to build predictors for ER, PR, HER2, and Ki67 markers. An in-house dataset was used, containing 92 multiparametric MRIs of patients with histologically proven BC and all four relevant biomarkers available. Thousands of radiomic features were extracted from post-contrast and subtracted Dynamic Contrast-Enanched (DCE) MRI images, Apparent Diffusion Coefficient (ADC) maps, and T2-weighted (T2) images. The two-step feature selection approach was used to identify significant radiomic features properly and then to build the final prediction models. They showed remarkable results in terms of F1-score for all the biomarkers: 84%, 63%, 90%, and 72% for ER, HER2, Ki67, and PR, respectively. When possible, the models were validated on the TCGA/TCIA Breast Cancer dataset, returning promising results (F1-score = 88% for the ER+/ER− classification task). The developed approach efficiently characterized BC heterogeneity according to the examined molecular biomarkers

Prognostic Value of Creatinine Levels at Admission on Disease Progression and Mortality in Patients with COVID-19-An Observational Retrospective Study

Introduction: Acute kidney disease and chronic kidney disease are considered conditions that can increase the mortality and severity of COVID-19. However, few studies have investigated the impact of creatinine levels on COVID-19 progression in patients without a history of chronic kidney disease. The aim of the study was to assess the impact of creatinine levels at hospital admission on COVID-19 progression and mortality. Methods: We performed a multicenter, observational, retrospective study involving seventeen COVID-19 Units in the Campania region in southern Italy. All adult (≥18 years) patients, hospitalized with a diagnosis of SARS-CoV-2 infection confirmed by a positive reverse transcriptase-polymerase chain reaction on a naso-oropharyngeal swab, from 28 February 2020 to 31 May 2021, were enrolled in the CoviCamp cohort. Results: Evaluating inclusion/exclusion criteria, 1357 patients were included. Considering in-hospital mortality and creatinine value at admission, the best cut-off point to discriminate a death during hospitalization was 1.115 mg/dL. The logistic regression demonstrated that factors independently associated with mortality were age (OR 1.082, CI: 1.054-1.110), Charlson Comorbidity Index (CCI) (OR 1.341, CI: 1.178-1.526), and an abnormal creatinine value at admission, defined as equal to or above 1.12 mg/dL (OR 2.233, CI: 1.373-3.634). Discussion: In conclusion, our study is in line with previous studies confirming that the creatinine serum level can predict mortality in COVID-19 patients and defining that the best cut-off of the creatinine serum level at admission to predict mortality was 1.12 mg/dL

Clinical Characterization of the Three Waves of COVID-19 Occurring in Southern Italy: Results of a Multicenter Cohort Study

Aims: To characterize patients hospitalized for COVID-19 in the three waves in Southern Italy. Methods: We conducted a multicenter observational cohort study involving seventeen COVID-19 Units in Campania, southern Italy: All adult (>= 18 years) patients, hospitalized with a diagnosis of SARS-CoV-2 infection from 28 February 2020 to 31 May 2021, were enrolled. Results: Two thousand and fifteen COVID-19 hospitalized patients were enrolled; 392 (19%) in the first wave, 917 (45%) in the second and 706 (35%) in the third wave. Patients showed a less severe clinical outcome in the first wave than in the second and third waves (73%, 65% and 72%, respectively; p = 0.003), but hospitalization expressed in days was longer in the first wave [Median (Q1-Q3): 17 (13-25) v.s. 14 (9-21) and 14 (9-19), respectively, p = 0.001)] and also mortality during hospitalization was higher in the first wave than in the second and third waves: 16.6% v.s. 11.3% and 6.5%, respectively (p = 0.0001). Multivariate analysis showed that older age [OR: 1.069, CI (1046-1092); p = 0.001], a worse Charlson comorbidity index [OR: 1042, CI (1233-1594; p = 0.0001] and enrolment during the first-wave [OR: 1.917, CI (1.054-3.485; p = 0.033] were predictors of mortality in hospitalized patients. Conclusions: Improved organization of the healthcare facilities and the increase in knowledge of clinical and therapeutic management have contributed to a trend in the reduction in mortality during the three waves of COVID-19

Lactate dehydrogenase and PaO2/FiO2 ratio at admission helps to predict CT score in patients with COVID-19: An observational study

Introduction: Since the beginning of the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) pandemic an important tool for patients with Coronavirus Disease 2019 (COVID-19) has been the computed tomography (CT) scan, but not always available in some settings The aim was to find a cut-off that can predict worsening in patients with COVID-19 assessed with a computed tomography (CT) scan and to find laboratory, clinical or demographic parameters that may correlate with a higher CT score. Methods: We performed a multi-center, observational, retrospective study involving seventeen COVID-19 Units in southern Italy, including all 321 adult patients hospitalized with a diagnosis of COVID-19 who underwent at admission a CT evaluated using Pan score. Results: Considering the clinical outcome and Pan score, the best cut-off point to discriminate a severe outcome was 12.5. High lactate dehydrogenase (LDH) serum value and low PaO2/FiO2 ratio (P/F) resulted independently associated with a high CT score. The Area Under Curve (AUC) analysis showed that the best cut-off point for LDH was 367.5 U/L and for P/F 164.5. Moreover, the patients with LDH> 367.5 U/L and P/F  164.5, 83.4%, vs 20%, respectively. Conclusions: A direct correlation was observed between CT score value and outcome of COVID-19, such as CT score and high LDH levels and low P/F ratio at admission. Clinical or laboratory tools that predict the outcome at admission to hospital are useful to avoiding the overload of hospital facilities

Pre-existing chronic kidney disease (CDK) was not associated with a severe clinical outcome of hospitalized COVID-19: results of a case-control study in Southern Italy

: The presence of co-morbidities is associated with a poor outcome in patients with COVID-19. The aim of the present study was to investigate the outcomes of patients with SARS-CoV-2 infection and chronic kidney disease (CKD) in order to assess its impact on mortality and severity of disease. We performed a multicenter, observational, 1:2 matched case-control study involving seventeen COVID-19 Units in southern Italy. All the adults hospitalized for SARS-CoV-2 infection and with pre-existing CKD were included (Cases). For each Case, two patients without CKD pair matched for gender, age (+5 years), and number of co-morbidities (excluding CKD) were enrolled (Controls). Of the 2,005 patients with SARS-CoV-2 infection followed during the study period, 146 patients with CKD and 292 patients without were enrolled in the case and control groups, respectively. Between the Case and Control groups, there were no statistically significant differences in the prevalence of moderate (17.1% vs 17.8%, p=0.27) or severe (18.8% and 13.7%, p=0.27) clinical presentation of COVID-19 or deaths (20.9% vs 28.1%, p=0.27). In the Case group, the patients dead during hospitalization were statistically higher in the 89 patients with CKD stage 4-5 compared to 45 patients with stages 1-3 CKD (30.3% vs 13.3%, p=0.03). Our data suggests that only CKD stage 4-5 on admission was associated with an increased risk of in-hospital death

Lactate dehydrogenase and PaO2/FiO2 ratio at admission helps to predict CT score in patients with COVID-19: An observational study

Introduction: Since the beginning of the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) pandemic an important tool for patients with Coronavirus Disease 2019 (COVID-19) has been the computed tomography (CT) scan, but not always available in some settings The aim was to find a cut-off that can predict worsening in patients with COVID-19 assessed with a computed tomography (CT) scan and to find laboratory, clinical or demographic parameters that may correlate with a higher CT score. Methods: We performed a multi-center, observational, retrospective study involving seventeen COVID-19 Units in southern Italy, including all 321 adult patients hospitalized with a diagnosis of COVID-19 who underwent at admission a CT evaluated using Pan score. Results: Considering the clinical outcome and Pan score, the best cut-off point to discriminate a severe outcome was 12.5. High lactate dehydrogenase (LDH) serum value and low PaO2/FiO2 ratio (P/F) resulted independently associated with a high CT score. The Area Under Curve (AUC) analysis showed that the best cut-off point for LDH was 367.5 U/L and for P/F 164.5. Moreover, the patients with LDH> 367.5 U/L and P/F < 164.5 showed more frequently a severe CT score than those with LDH< 367.5 U/L and P/F> 164.5, 83.4%, vs 20%, respectively. Conclusions: A direct correlation was observed between CT score value and outcome of COVID-19, such as CT score and high LDH levels and low P/F ratio at admission. Clinical or laboratory tools that predict the outcome at admission to hospital are useful to avoiding the overload of hospital facilities

An antipsychotic drug exerts anti-prion effects by altering the localization of the cellular prion protein

<div><p>Prion diseases are neurodegenerative conditions characterized by the conformational conversion of the cellular prion protein (PrP^C), an endogenous membrane glycoprotein of uncertain function, into PrP^Sc, a pathological isoform that replicates by imposing its abnormal folding onto PrP^C molecules. A great deal of evidence supports the notion that PrP^C plays at least two roles in prion diseases, by acting as a substrate for PrP^Sc replication, and as a mediator of its toxicity. This conclusion was recently supported by data suggesting that PrP^C may transduce neurotoxic signals elicited by other disease-associated protein aggregates. Thus, PrP^C may represent a convenient pharmacological target for prion diseases, and possibly other neurodegenerative conditions. Here, we sought to characterize the activity of chlorpromazine (CPZ), an antipsychotic previously shown to inhibit prion replication by directly binding to PrP^C. By employing biochemical and biophysical techniques, we provide direct experimental evidence indicating that CPZ does not bind PrP^C at biologically relevant concentrations. Instead, the compound exerts anti-prion effects by inducing the relocalization of PrP^C from the plasma membrane. Consistent with these findings, CPZ also inhibits the cytotoxic effects delivered by a PrP mutant. Interestingly, we found that the different pharmacological effects of CPZ could be mimicked by two inhibitors of the GTPase activity of dynamins, a class of proteins involved in the scission of newly formed membrane vesicles, and recently reported as potential pharmacological targets of CPZ. Collectively, our results redefine the mechanism by which CPZ exerts anti-prion effects, and support a primary role for dynamins in the membrane recycling of PrP^C, as well as in the propagation of infectious prions.</p></div