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Few data are available on incidence of multidrug-resistant organism (MDRO) colonization and infections in mechanically ventilated patients, particularly during the COVID-19 pandemic. We retrospectively evaluated all patients admitted to the COVID-19 intensive care unit (ICU) of Hub Hospital in Milan, Italy, during October 2020‒May 2021. Microbiologic surveillance was standardized with active screening at admission and weekly during ICU stay. Of 435 patients, 88 (20.2%) had MDROs isolated ≤48 h after admission. Of the remaining patients, MDRO colonization was diagnosed in 173 (51.2%), MDRO infections in 95 (28.1%), and non-MDRO infections in 212 (62.7%). Non-MDRO infections occurred earlier than MDRO infections (6 days vs. 10 days; p<0.001). Previous exposure to antimicrobial drugs within the ICU was higher in MDRO patients than in non-MDRO patients (116/197 [58.9%] vs. 18/140 [12.9%]; p<0.001). Our findings might serve as warnings for future respiratory viral pandemics and call for increased measures of antimicrobial stewardship and infection control.

Few data are available on incidence of multidrug-resistant organism (MDRO) colonization and infections in mechanically ventilated patients, particularly during the COVID-19 pandemic. We retrospectively evaluated all patients admitted to the COVID-19 intensive care unit (ICU) of Hub Hospital in Milan, Italy, during October 2020‒May 2021. Microbiologic surveillance was standardized with active screening at admission and weekly during ICU stay. Of 435 patients, 88 (20.2%) had MDROs isolated ≤48 h after admission. Of the remaining patients, MDRO colonization was diagnosed in 173 (51.2%), MDRO infections in 95 (28.1%), and non-MDRO infections in 212 (62.7%). Non-MDRO infections occurred earlier than MDRO infections (6 days vs. 10 days; p<0.001). Previous exposure to antimicrobial drugs within the ICU was higher in MDRO patients than in non-MDRO patients (116/197 [58.9%] vs. 18/140 [12.9%]; p<0.001). Our findings might serve as warnings for future respiratory viral pandemics and call for increased measures of antimicrobial stewardship and infection control.Few data are available on incidence of multidrug-resistant organism (MDRO) colonization and infections in mechanically ventilated patients, particularly during the COVID-19 pandemic. We retrospectively evaluated all patients admitted to the COVID-19 intensive care unit (ICU) of Hub Hospital in Milan, Italy, during October 2020‒May 2021. Microbiologic surveillance was standardized with active screening at admission and weekly during ICU stay. Of 435 patients, 88 (20.2%) had MDROs isolated ≤48 h after admission. Of the remaining patients, MDRO colonization was diagnosed in 173 (51.2%), MDRO infections in 95 (28.1%), and non-MDRO infections in 212 (62.7%). Non-MDRO infections occurred earlier than MDRO infections (6 days vs. 10 days; p<0.001). Previous exposure to antimicrobial drugs within the ICU was higher in MDRO patients than in non-MDRO patients (116/197 [58.9%] vs. 18/140 [12.9%]; p<0.001). Our findings might serve as warnings for future respiratory viral pandemics and call for increased measures of antimicrobial stewardship and infection control.

Few data are available on incidence of multidrug-resistant organism (MDRO) colonization and infections in mechanically ventilated patients, particularly during the COVID-19 pandemic. We retrospectively evaluated all patients admitted to the COVID-19 intensive care unit (ICU) of Hub Hospital in Milan, Italy, during October 2020‒May 2021. Microbiologic surveillance was standardized with active screening at admission and weekly during ICU stay. Of 435 patients, 88 (20.2%) had MDROs isolated ≤48 h after admission. Of the remaining patients, MDRO colonization was diagnosed in 173 (51.2%), MDRO infections in 95 (28.1%), and non-MDRO infections in 212 (62.7%). Non-MDRO infections occurred earlier than MDRO infections (6 days vs. 10 days; p<0.001). Previous exposure to antimicrobial drugs within the ICU was higher in MDRO patients than in non-MDRO patients (116/197 [58.9%] vs. 18/140 [12.9%]; p<0.001). Our findings might serve as warnings for future respiratory viral pandemics and call for increased measures of antimicrobial stewardship and infection control.

Background. Developmental Language Disorder (DLD) is frequent in childhood and may have long-term sequelae. By employing an evidence-based approach, this scoping review aims at identifying (a) early predictors of DLD; (b) the optimal age range for the use of screening and diagnostic tools; (c) effective diagnostic tools in preschool children. Methods. We considered systematic reviews, meta-analyses, and primary observational studies with control groups on predictive, sensitivity and specificity values of screening and diagnostic tools and psycholinguistic measures for the assessment of DLD in preschool children. We identified 37 studies, consisting of 10 systematic reviews and 27 primary studies. Results. Delay in gesture production, receptive and/or expressive vocabulary, syntactic comprehension, or word combination up to 30 months emerged as early predictors of DLD, a family history of DLD appeared to be a major risk factor, and low socioeconomic status and environmental input were reported as risk factors with lower predictive power. Optimal time for screening is suggested between age 2 and 3, for diagnosis around age 4. Because of the high variability of sensitivity and specificity values, joint use of standardized and psycholinguistic measures is suggested to increase diagnostic accuracy. Conclusions. Monitoring risk situations and employing caregivers’ reports, clinical assessment and multiple linguistic measures are fundamental for an early identification of DLD and timely interventions.

Walnuts (Juglans regia L.) are recognized for their rich nutritional profile and health-promoting properties. This study investigates the impact of biostimulation, via wood distillate application, on the chemical composition and therapeutic potential of walnut kernels, focusing on their wound healing activity. Biostimulated walnuts showed enhanced levels of phenolic compounds including antioxidants, sugars, and amino acids compared to untreated or agrochemically treated controls. Phytocomplexes extracted from walnut kernels using green methodologies were tested on human keratinocytes (HaCaT), revealing pro-migratory effect, boosted by biostimulation. Molecular analyses demonstrated the activation of epithelial–mesenchymal transition (EMT) pathways, marked by downregulation of epithelial markers and upregulation of mesenchymal genes such as MMP3, MMP9, vimentin, and SMAD3. Furthermore, a synergistic effect was observed between extracts derived from biostimulated kernels and manuka honey in promoting wound closure. These findings suggest that kernels from biostimulated walnuts may serve as functional foods, paving the way for their use in regenerative medicine.

Network Intrusion Detection Systems (NIDS) play an important role as tools for identifying potential network threats. In the context of ever-increasing traffic volume on computer networks, flow-based NIDS arise as good solutions for real-time traffic classification. In recent years, different flow-based classifiers have been proposed using Machine Learning (ML) algorithms. Nevertheless, classical ML-based classifiers have some limitations. For instance, they require large amounts of labeled data for training, which might be difficult to obtain. Additionally, most ML-based classifiers are not capable of domain adaptation, i.e. after being trained on an specific data distribution, they are not general enough to be applied to other related data distributions. And, finally, many of the models inferred by these algorithms are black boxes, which do not provide explainable results. To overcome these limitations, we propose a new algorithm, called Energy-based Flow Classifier (EFC). This anomaly-based classifier uses inverse statistics to infer a statistical model based on labeled benign examples. We show that EFC is capable of accurately performing binary flow classification and is more adaptable to different data distributions than classical ML-based classifiers. Given the positive results obtained on three different datasets (CIDDS-001, CICIDS17 and CICDDoS19), we consider EFC to be a promising algorithm to perform robust flow-based traffic classification.

Several machine learning-based Network Intrusion Detection Systems (NIDS) have been proposed in recent years. Still, most of them were developed and evaluated under the assumption that the training context is similar to the test context. This assumption is false in real networks, given the emergence of new attacks and variants of known attacks. To deal with this reality, the open set recognition field, which is the most general task of recognizing classes not seen during training in any domain, began to gain importance in machine learning based NIDS research. Yet, existing solutions are often bound to high temporal complexities and performance bottlenecks. In this work, we propose an algorithm to be used in NIDS that performs open set recognition. Our proposal is an adaptation of the single-class Energy-based Flow Classifier (EFC), which proved to be an algorithm with strong generalization capability and low computational cost. The new version of EFC correctly classifies not only known attacks, but also unknown ones, and differs from other proposals from the literature by presenting a single layer with low temporal complexity. Our proposal was evaluated against well-established multi-class algorithms and as an open set classifier. It proved to be an accurate classifier in both evaluations, similar to the state of the art. As a conclusion of our work, we consider EFC a promising algorithm to be used in NIDS for its high performance and applicability in real networks.

“Nursing home residents” are the most exposed to polypharmacy because they are very elderly, disabled and affected by many diseases either acute and chronic. Simultaneous use of many medicines could be a risk factor for an inappropriate use of drugs that could cause adverse reactions to medicines, increased morbidity and increased use of health care resources. From this context comes the need by Emilia Romagna, to fund an “Active Pharmacovigilance” project about these patients and with the following objectives: to assess the appropriateness of prescribing drugs affecting the cardiovascular system and nervous system, detect the risk of exposure to potential drug interactions, detect prescriptions outside the handbook used by the structures involved and encourage the reporting of suspected adverse reactions by health professionals involved in the project. Six Local Health of Emilia Romagna Region have participated in the project: Modena, Bologna, Parma, Reggio Emilia, Imola and Cesena for a total of 52 nursing homes affiliated involved. The project lasted two years and were recognized therapies of 3106 patients in 2012 and 2,970 in 2013. In 72.8% of cases were women patients and in 27.2% men, age average was 83,8 years. Patients were treated with an average of 9.7 medications / day and medicinal products that affect the nervous system have been the most prescribed (93.94% of total patients in 2013). During two-year project, off label prescriptions fell from 2.8% to 2.5% as well as decreased the number of active ingredients used off label from 31 to 25. Drug Interactions decreased both in the number of different types (78 to 74), both in terms of percentage of patients exposed (49.9% to 49.5%). Finally, a good result was achieved in the field of reporting suspected ADRs: 5 reports received in 2012, there was an increase to 31 in 2013. During the two-year project the presence of the pharmacist in the structure was welcomed by Clinicians and Health Professionals. The collaboration between professionals has led to an improvement of prescriptive appropriateness to a safer use of medicines and greater sensitivity in reporting suspected Adverse Drug Reactions to medicines.

Network intrusion detection systems (NIDS) are one of many solutions that make up a computer security system. Several machine learning-based NIDS have been proposed in recent years, but most of them were developed and evaluated under the assumption that the training context is similar to the test context. In real networks, this assumption is false, given the emergence of new attacks and variants of known attacks. To deal with this reality, the open set recognition field, which is the most general task of recognizing classes not seen during training in any domain, began to gain importance in NIDS research. Yet, existing solutions are often bounded to high temporal complexities and performance bottlenecks. In this work, we propose an algorithm to be used in NIDS that performs open set recognition. Our proposal is an adaptation of the single-class Energy-based Flow Classifier (EFC), which proved to be an algorithm with strong generalization capability and low computational cost. The new version of EFC correctly classifies not only known attacks, but also unknown ones, and differs from other proposals from the literature by presenting a single layer with low temporal complexity. Our proposal was evaluated against well-established multi-class algorithms and as an open set classifier. It proved to be an accurate classifier in both evaluations, similar to the state of the art. As a conclusion of our work, we consider EFC a promising algorithm to be used in NIDS for its high performance and applicability in real networks.

Microglia reactivity entails a large-scale remodeling of cellular geometry, but the role of the microtubule cytoskeleton during these changes remains unexplored. Here we show that reactive proinflammatory microglia provide a heretofore unique example of microtubule reorganization from a non-centrosomal array of parallel and stable microtubules to a radial array of more dynamic microtubules. While in the homeostatic state microglia nucleate microtubules at Golgi outposts, proinflammatory signaling induces recruitment of nucleating material nearby the centrosome and inhibition of centrosomal maturation enhances NLRP3 inflammasome activation and secretion of IL-1β. Our results demonstrate that a hallmark of microglia reactivity is a striking remodeling of the microtubule cytoskeleton and suggest that pericentrosomal microtubule nucleation may serve as a distinct marker of microglia activation as well as a novel target to modulate cytokine-mediated inflammatory responses in chronic disease and tissue injury.