Microbiological surveillance of hospital ventilation systems in departments at high risk of nosocomial infections

The air in hospital wards with patients at high risk (Surgeries, Intensive Care Units and Bone Marrow Transplant Centers) has been surveyed less than the one in Operating Rooms. Therefore in this study we considered useful to verify the microbic contamination of the air of those wards evaluating the consistency of ventilation systems in relation also to the presence and location of HEPA absolute filters. Seven departments of Genoese San Martino Hospital at high risk of infection were taken into account. In there, environmental investigations have been performed by air samplings and by analyzing bacterial and fungal growth on plates after an incubation period. Almost 60% of all samples taken in wards yielded a positive result and the average values of bacterial and aspergillar charges measured at air flow emission openings decisively exceed the ones considered standard in operating rooms. Still, the average values of airborne bacterial charges were significantly higher in those wards equipped with central filters (p inf. 0.001), while as far as the aspergillar charge is concerned, no statistically relevant differences were noticed. In wards with ventilation system, the bacterial charge value raises from the emission grids to the middle of the room and to the aspiration grids, while the ward not equipped with a ventilation system presents in the middle of the room an average bacterial charge 2 to 10 times higher than the one in other wards. The average values regarding bacterial and aspergillar charges resulted quite high in all the departments surveyed. Nevertheless, if we take into account ventilation systems equipped with absolute filters HEPA located centrally or peripherally, it can be outlined that the air quality from the point of view of both microbic and aspergillar contamination turns out to be decisively better in systems with peripheral filters. Moreover, a compared analysis of the three Hematology wards allows us to infer that the presence of artificial ventilation systems can lower the bacterial and fungal compared with a ward with natural ventilation

Performance of three model-based iterative reconstruction algorithms using a CT task-based image quality metric

In this study we evaluated the task-based image quality of a low contrast clinical task for the abdomen protocol (e.g., pancreatic tumour) of three different CT vendors, exploiting three model-based iterative reconstruction (MBIR) levels. We used three CT systems equipped with a full, partial, advanced MBIR algorithms. Acquisitions were performed on a phantom at three dose levels. Acquisitions were reconstructed with a standard kernel, using filtered back projection algorithm (FBP) and three levels of the MBIR. The noise power spectrum (NPS), the normalized one (nNPS) and the task-based transfer function (TTF) were computed following the method proposed by the American Association of Physicists in Medicine task group report-233 (AAPM TG-233). Detectability index (d') of a small lesion (small feature; 100 HU and 5-mm diameter) was calculated using non-prewhitening with eye-filter model observer (NPWE).The nNPS, NPS and TTF changed differently depending on CT system. Higher values of d' were obtained with advanced-MBIR, followed by full-MBIR and partial-MBIR.Task-based image quality was assessed for three CT scanners of different vendors, considering a clinical question. Detectability can be a tool for protocol optimisation and dose reduction since the same dose levels on different scanners correspond to different d' values.Comment: 7 pages, 5 figures, 3 table

The hepatitis D virus in Italy. A vanishing infection, not yet a vanished disease

Introduction: Hepatitis D Virus (HDV) infection is vanishing in Italy. It is therefore believed that hepatitis D is no longer a medical problem in the domestic population of the country but remains of concern only in migrants from HDV-endemic areas. Objectives: To report the clinical features and the medical impact of the residual domestic HDV infections in Italy. Methods: From 2010 to 2019, one hundred ninety-three first-time patients with chronic HDV liver disease attended gastroenterology units in Torino and San Giovanni Rotondo (Apulia); 121 were native Italians and 72 were immigrants born abroad. For this study, we considered the 121 native Italians in order to determine their clinical features and the impact of HDV disease in liver transplant programs. Results: At the last observation the median age of the 121 native Italians was 58 years. At the end of the follow-up, the median liver stiffness was 12.0 kPa (95% CI 11.2–17.4), 86 patients (71.1%) had a diagnosis of cirrhosis; 80 patients (66.1%) remained HDV viremic. The ratio of HDV to total HBsAg transplants varied from 38.5% (139/361) in 2000–2009 to 50.2% (130/259) in 2010–2019, indicating a disproportionate role of hepatitis D in liver transplants compared to the minor prevalence of HDV infections in the current scenario of HBsAg-positive liver disorders in Italy. Conclusion: Though HDV is vanishing in Italy, a legacy of ageing native-Italian patients with advanced HDV liver disease still represents an important medical issue and maintains an impact on liver transplantation

Radiomics predicts response of individual HER2-amplified colorectal cancer liver metastases in patients treated with HER2-targeted therapy

The aim of our study was to develop and validate a machine learning algorithm to predict response of individual HER2-amplified colorectal cancer liver metastases (lmCRC) undergoing dual HER2-targeted therapy. Twenty-four radiomics features were extracted after 3D manual segmentation of 141 lmCRC on pretreatment portal CT scans of a cohort including 38 HER2-amplified patients; feature selection was then performed using genetic algorithms. lmCRC were classified as nonresponders (R−), if their largest diameter increased more than 10% at a CT scan performed after 3 months of treatment, responders (R+) otherwise. Sensitivity, specificity, negative (NPV) and positive (PPV) predictive values in correctly classifying individual lesion and overall patient response were assessed on a training dataset and then validated on a second dataset using a Gaussian naïve Bayesian classifier. Per-lesion sensitivity, specificity, NPV and PPV were 89%, 85%, 93%, 78% and 90%, 42%, 73%, 71% respectively in the testing and validation datasets. Per-patient sensitivity and specificity were 92% and 86%. Heterogeneous response was observed in 9 of 38 patients (24%). Five of nine patients were carriers of nonresponder lesions correctly classified as such by our radiomics signature, including four of seven harboring only one nonresponder lesion. The developed method has been proven effective in predicting behavior of individual metastases to targeted treatment in a cohort of HER2 amplified patients. The model accurately detects responder lesions and identifies nonresponder lesions in patients with heterogeneous response, potentially paving the way to multimodal treatment in selected patients. Further validation will be needed to confirm our findings

Glutamine Synthetase 1 Increases Autophagy Lysosomal Degradation of Mutant Huntingtin Aggregates in Neurons, Ameliorating Motility in a Drosophila Model for Huntington's Disease

Glutamine Synthetase 1 (GS1) is a key enzyme that catalyzes the ATP-dependent synthesis of l-glutamine from l-glutamate and is also member of the Glutamate Glutamine Cycle, a complex physiological process between glia and neurons that controls glutamate homeostasis and is often found compromised in neurodegenerative diseases including Huntington's disease (HD). Here we report that the expression of GS1 in neurons ameliorates the motility defects induced by the expression of the mutant Htt, using a Drosophila model for HD. This phenotype is associated with the ability of GS1 to favor the autophagy that we associate with the presence of reduced Htt toxic protein aggregates in neurons expressing mutant Htt. Expression of GS1 prevents the TOR activation and phosphorylation of S6K, a mechanism that we associate with the reduced levels of essential amino acids, particularly of arginine and asparagine important for TOR activation. This study reveals a novel function for GS1 to ameliorate neuronal survival by changing amino acids' levels that induce a "starvation-like" condition responsible to induce autophagy. The identification of novel targets that inhibit TOR in neurons is of particular interest for the beneficial role that autophagy has in preserving physiological neuronal health and in the mechanisms that eliminate the formation of toxic aggregates in proteinopathies

Delta-Radiomics Predicts Response to First-Line Oxaliplatin-Based Chemotherapy in Colorectal Cancer Patients with Liver Metastases

SIMPLE SUMMARY: Oxaliplatin-based chemotherapy remains the mainstay of first-line therapy in patients with metastatic colorectal cancer (mCRC). Unfortunately, only approximately 60% of treated patients achieve response, and half of responders will experience an early onset of disease progression. Furthermore, some individuals will develop a mixed response due to the emergence of resistant tumor subclones. The ability to predicting which patients will acquire resistance could help them avoid the unnecessary toxicity of oxaliplatin therapies. Furthermore, sorting out lesions that do not respond, in the context of an overall good response, could trigger further investigation into their mutational landscape, providing mechanistic insight towards the planning of a more comprehensive treatment. In this study, we validated a delta-radiomics signature capable of predicting response to oxaliplatin-based first-line treatment of individual liver colorectal cancer metastases. Findings could pave the way to a more personalized treatment of patients with mCRC. ABSTRACT: The purpose of this paper is to develop and validate a delta-radiomics score to predict the response of individual colorectal cancer liver metastases (lmCRC) to first-line FOLFOX chemotherapy. Three hundred one lmCRC were manually segmented on both CT performed at baseline and after the first cycle of first-line FOLFOX, and 107 radiomics features were computed by subtracting textural features of CT at baseline from those at timepoint 1 (TP1). LmCRC were classified as nonresponders (R−) if they showed progression of disease (PD), according to RECIST1.1, before 8 months, and as responders (R+), otherwise. After feature selection, we developed a decision tree statistical model trained using all lmCRC coming from one hospital. The final output was a delta-radiomics signature subsequently validated on an external dataset. Sensitivity, specificity, positive (PPV), and negative (NPV) predictive values in correctly classifying individual lesions were assessed on both datasets. Per-lesion sensitivity, specificity, PPV, and NPV were 99%, 94%, 95%, 99%, 85%, 92%, 90%, and 87%, respectively, in the training and validation datasets. The delta-radiomics signature was able to reliably predict R− lmCRC, which were wrongly classified by lesion RECIST as R+ at TP1, (93%, averaging training and validation set, versus 67% of RECIST). The delta-radiomics signature developed in this study can reliably predict the response of individual lmCRC to oxaliplatin-based chemotherapy. Lesions forecasted as poor or nonresponders by the signature could be further investigated, potentially paving the way to lesion-specific therapies