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Roads represent very important infrastructure and play a significant role in economic, cultural, and social growth. Therefore, there is a critical need for many researchers to model crash injury severity in order to study how safe roads are. When measuring the cost of crashes, the severity of the crash is a critical criterion, and it is classified into various categories. The number of vehicles involved in the crash (NVIC) is a crucial factor in all of these categories. For this purpose, this research examines road safety and provides a prediction model for the number of vehicles involved in a crash. Specifically, learning vector quantization (LVQ 2.1), one of the sub-branches of artificial neural networks (ANNs), is used to build a classification model. The novelty of this study demonstrates LVQ 2.1’s efficacy in categorizing accident data and its ability to improve road safety strategies. The LVQ 2.1 algorithm is particularly suitable for classification tasks and works by adjusting prototype vectors to improve the classification performance. The research emphasizes how urgently better prediction algorithms are needed to handle issues related to road safety. In this study, a dataset of 564 crash records from rural roads in Calabria between 2017 and 2048, a region in southern Italy, was utilized. The study analyzed several key parameters, including daylight, the crash type, day of the week, location, speed limit, average speed, and annual average daily traffic, as input variables to predict the number of vehicles involved in rural crashes. The findings revealed that the “crash type” parameter had the most significant impact, whereas “location” had the least significant impact on the occurrence of rural crashes in the investigated areas.

Background: Serodiagnosis of TORCH infections should be performed in pre-pregnancy and reproductive-age women to prevent vertical transmission. Herein, we conducted a 5-year cross-sectional retrospective study in childbearing age women to provide prevalence data. Also, stratifying the cohort into three age groups, we identified those most susceptible to acute TORCH infections. Methods: Between 2019 and 2023, serum samples from 2286 childbearing age women attending the “R. Dulbecco” University Hospital of Catanzaro were collected. Screening for TORCH pathogens, such as: Toxoplasma gondii (TOX), Cytomegalovirus (CMV), Rubella Virus (RUB), Parvovirus B19 (ParvoB19), Herpes Simplex Virus types 1 and 2 (HSV1, HSV2) and Treponema pallidum was carried out using serological tests. Chemiluminescent immunoassay was performed to detect TOX, CMV and ParvoB19 Immunoglobulin M (IgM) and Immunoglobulin G (IgG) antibodies, while Enzyme Linked Fluorescent Assay was performed to detect RUB IgM and IgG antibodies and CMV and TOX IgG Avidity. Enzyme Linked Immunosorbent Assay was performed to detect HSV1 IgG, HSV2 IgG, HSV1/2 IgM, T. pallidum total antibodies and RUB IgG Avidity. Binomial logistic regression models were developed to compare seroprevalence rates among different age groups. Results: The highest immunological protection was observed for RUB infection (87 %), probably associated with vaccination practice, followed by HSV1 and CMV (82 % and 63 %). The 16–25 year age group results as the most susceptible to acute infections as demonstrated by odds of CMV IgM positivity (primary infection) which decreased with age. Conclusions: The TORCH serological screening program should be implemented in women before pregnancy to formulate strategies for serological screening of childbearing age women and guiding clinicians in making decisions.

The rapid advancement of Connected Autonomous Vehicles (CAVs) equipped with self-powered sensors is poised to revolutionize road safety, efficiency, and the quality of travel. However, the effective integration of these technologies within dynamic road environments poses significant challenges, highlighting the need for innovative multi-criteria decision-making (MCDM) approaches to optimize their deployment. This study tries to solve the integration problem by proposing an MCDM method that uses fuzzy sets to evaluate and rank different scenarios for better performance of augmented intelligence and the Internet of Things (IoT) in CAVs. This research uses two key techniques: the Fuzzy Logarithm of Incremental Weights (F-LMAW) method for criteria evaluation and the Fermatean Fuzzy Weighted Aggregated Sum Product Assessment (FF-WASPAS) method for scenario evaluation. To ensure the reliability and accuracy of the results, a sensitivity analysis was conducted. The analysis confirmed the effectiveness of the proposed approach. The study's results showed that the third scenario (the integration of augmented intelligence and IoT in urban areas via self-powered sensors) got the highest score, which shows how important it is compared to the other choices. The obtained results highlight the importance of integrating augmented intelligence and IoT technologies to enhance public transportation with autonomous vehicles equipped with self-powered sensors.

Background: Inflammatory bowel disease (IBD) is characterized by persistent inflammation and is often associated with metabolic dysfunction-associated steatotic liver disease (MASLD). IBD patients are at risk of developing MASLD due to shared risk factors such as gut dysbiosis and systemic inflammation. The new MASLD nomenclature emphasizes the link between liver steatosis and cardiometabolic comorbidities. However, the prevalence of MASLD in IBD patients remains poorly explored. The main aim of this cross-sectional study is to assess the prevalence of ultrasound (US) and the clinical features of MASLD in patients with IBDs. Materials and Methods: We conducted a retrospective study enrolling 272 Italian IBD patients attending Renato Dulbecco Teaching Hospital in a period between 1 January 2021 and 31 December 2023. MASLD was diagnosed based on the presence of liver steatosis with cardiometabolic risk factors, using established guidelines. Demographic, clinical, and laboratory data were collected and analyzed. Statistical significance was determined at a p-value < 0.05. Results: Of the 272 IBD patients, 6% had non-alcoholic fatty liver disease (NAFLD), while 18% had MASLD. Patients with IBD-MASLD were significantly older, had higher body mass index, waist circumference, and triglyceride levels, and were more likely to have type 2 diabetes mellitus and hypertension compared to those with IBD-NAFLD. IBD-MASLD patients also showed higher disease activity scores and required more frequent surgical interventions. Bivariate logistic regression revealed triglyceride levels as a significant predictor of MASLD in IBD patients. Conclusions: MASLD is more prevalent in IBD patients, highlighting the importance of early detection of liver steatosis in this at-risk population. The association between MASLD and cardiometabolic risk factors underscores the need for a multidisciplinary approach to manage these patients effectively. Further studies in larger cohorts are necessary to confirm these findings and explore the pathophysiological mechanisms involved.

Road safety is an important subject to study in both the technical and academic fields of road transportation. In recent years, there has been a significant rise in the number of studies that look at how intelligent transportation systems (ITS) can be used and what role it plays in making roads safer in different countries. Nevertheless, there are still relatively few in-depth quantitative and qualitative analyses published on the topic of ITS's role in ensuring road safety. For this purpose, the main goal of this study is to look at a thorough quantitative and qualitative analysis of how ITS is used in road safety as a part of transportation engineering. In this study, we reviewed the scientific studies done on the use of ITS in studies of road safety from 1990s to 2022. These studies were published in journals or presented at conferences that were part of the Web of Science (WoS) Index. The analysis in this study gives a thorough map of the field, showing how it has changed over time and pointing the way to new areas of research.

Superior mesenteric artery (SMA) syndrome or Wilkie’s syndrome is a vascular compression disorder that causes the abnormal compression of the third portion of the duodenum by the SMA. It has a low incidence rate, which is higher in young women, and is rarely associated with the Nutcracker phenomenon: a condition of the compression of the left renal vein between the SMA and the aorta, which manifests as pain in the left flank and pelvis. Here, we report on the case of a 54-year-old woman with a history of repeated episodes of abdominal pain caused by the Nutcracker syndrome and Wilkie’s syndrome.

Background: Clostridioides difficile infection (CDI) is a significant cause of mortality, especially in healthcare environments. Reliable biomarkers that can accurately predict mortality in CDI patients are yet to be evaluated. Our study aims to evaluate the accuracy of several inflammatory biomarkers and hemogram-derived ratios in predicting mortality in CDI patients, such as the neutrophil-to-lymphocyte ratio (NLR), the systemic immune-inflammation index (SII), the platelet-to-neutrophil ratio (PNR), the derived neutrophil-to-lymphocyte ratio (dNLR), C-reactive protein (CRP), the platelet-to-lymphocyte ratio (PLR), and procalcitonin (PCT). Results: NLR showed a sensitivity of 72.5% and a specificity of 58.42% with an area under curve (AUC) = 0.652. SII had a sensitivity of 77.5%, a specificity of 54.74%, and an AUC = 0.64. PNR, neutrophils, dNLR, and lymphocytes had lower AUCs which ranged from 0.595 to 0.616, with varied sensitivity and specificity. CRP, leukocytes, and platelets showed modest predictive values with AUCs below 0.6. PCT had a sensitivity of 100%, a low specificity of 7.41%, and an AUC = 0.528. Methods: We conducted a retrospective analysis of CDI patients from two different hospital settings in Italy and Romania during the COVID-19 pandemic, from 1 January 2020 to 5 May 2023. Statistical analyses included t-tests, Wilcoxon rank-sum tests, χ2 tests, and multivariate logistic regression to identify predictors of mortality. ROC analysis assessed the accuracy of biomarkers and hemogram-derived ratios. A p value < 0.05 was considered significant. Conclusions: Neutrophils, dNLR, NLR, SII, and PNR are valuable biomarkers for predicting mortality in CDI patients. Understanding these predictors can improve risk stratification and clinical outcomes for CDI patients.

Background: Liver cancer, particularly hepatocellular carcinoma (HCC), is a significant gastrointestinal disease with a mortality rate as high as nearly 80% within five years. The disease’s pathophysiology involves deranged immune responses and bile acid metabolism, with the gut microbiota (GM) playing a crucial role. Recent research highlights the potential of GM in influencing HCC treatment outcomes, especially regarding immune checkpoint inhibitors (ICIs). However, few patients currently benefit from ICIs due to a lack of effective response biomarkers. Aims and methods: This review aimed to explore the literature on HCC treatment issues, focusing on immune response, bile acid metabolism, and GM dysbiosis. This review included studies from PubMed, Medline, and major gastroenterology and hepatology meetings, using keywords like gut microbiota, immune system, liver cancer, and checkpoint inhibitors. Results: GM dysbiosis significantly impacts immune response and bile acid metabolism, making it a promising biomarker for ICI response. Modulating GM can enhance ICI treatment efficacy, although more research is needed to confirm its direct therapeutic benefits for HCC. Conclusions: GM dysbiosis is integral to liver cancer pathogenesis and treatment response. Its modulation offers promising therapeutic avenues for improving HCC prognosis and response to immunotherapy.

Helicobacter pylori (H. pylori) infection is a prevalent global health issue, associated with several gastrointestinal disorders, including gastritis, peptic ulcers, and gastric cancer. The landscape of H. pylori treatment has evolved over the years, with increasing challenges due to antibiotic resistance and treatment failure. Traditional diagnostic methods, such as the urea breath test, stool antigen test, and endoscopy with biopsy, are commonly used in clinical practice. However, the emergence of antibiotic-resistant strains has led to a decline in treatment efficacy, necessitating a re-evaluation of common diagnostic tools. This narrative review aims to explore the possible changes in the diagnostic approach of H. pylori infection in the era of treatment failure. Molecular techniques, including polymerase chain reaction and whole genome sequencing, which have high sensitivity and specificity, allow the detection of genes associated with antibiotic resistance. On the other hand, culture isolation and a phenotypic antibiogram could be used in the diagnostic routine, although H. pylori is a fastidious bacterium. However, new molecular approaches are promising tools for detecting the pathogen and its resistance genes. In this regard, more real-life studies are needed to reveal new diagnostic tools suitable for identifying multidrug-resistant H. pylori strains and for outlining proper treatment.

Alcoholic liver disease (ALD) is a major cause of chronic liver disease. This term covers a broad spectrum of liver lesions, from simple steatosis to alcoholic hepatitis and cirrhosis. The pathogenesis of ALD is multifactorial and not fully elucidated due to complex mechanisms related to direct ethanol toxicity with subsequent hepatic and systemic inflammation. The accumulation of pro-inflammatory cytokines and the reduction of anti-inflammatory cytokines promote the development and progression of ALD. To date, there are no targeted therapies to counter the progression of chronic alcohol-related liver disease and prevent acute liver failure. Corticosteroids reduce mortality by acting on the hepatic-systemic inflammation. On the other hand, several studies analyzed the effect of inhibiting pro-inflammatory cytokines and stimulating anti-inflammatory cytokines as potential therapeutic targets in ALD. This narrative review aims to clarify the role of the main cytokines involved in the pathogenesis and treatment of ALD.

The gastrointestinal tract is inhabited by the gut microbiota. The main phyla are Firmicutes and Bacteroidetes. In non-alcoholic fatty liver disease, now renamed metabolic dysfunction-associated fatty liver disease (MAFLD), an alteration in Firmicutes and Bacteroidetes abundance promotes its pathogenesis and evolution into non-alcoholic steatohepatitis, liver cirrhosis, and hepatocellular carcinoma. For this reason, early treatment is necessary to counteract its progression. The aim of the present narrative review is to evaluate the different therapeutic approaches to MAFLD. The most important treatment for MAFLD is lifestyle changes. In this regard, the Mediterranean diet could be considered the gold standard in the prevention and treatment of MAFLD. In contrast, a Western diet should be discouraged. Probiotics and fecal microbiota transplantation seem to be valid, safe, and effective alternatives for MAFLD treatment. However, more studies with a longer follow-up and with a larger cohort of patients are needed to underline the more effective approaches to contrasting MAFLD.

With more cars on the road and an increasing travel rate, one of the main issues in transportation engineering is how to make roads safe. The evaluation of the level of road crash severity is a significant component of the road safety assessment in terms of financial and personal losses. One of the most advanced ways to control and manage traffic information in modern traffic management systems is through intelligent transportation systems (ITS). ITS plays a big part in improving road safety. On the other hand, machine learning techniques play an important role in increasing the level of ITS performance. Hence, this study aims to predict the level of crash severity through a classification approach. In this study, two machine learning techniques namely artificial neural network (ANN) and Grey Wolf optimization algorithm (GWO)–ANN were developed to predict the level of road crash severity, and several performance metrics were used to assess the predictive algorithms' predictive capability. For this purpose, some factors that affect the level of severity were considered, including light condition (LC), weekday (W), horizontal alignment (HA), road element (RE), type of crash (TC), vehicle type (VT), driver gender (DG), average speed (AS) and annual average daily traffic (AADT). 202 case studies were recorded from urban roads in Calabria in southern Italy. When comparing the proposed GWO–ANN model to the ANN model, the findings clearly demonstrated the superiority of the proposed GWO–ANN model.

Background and aim: Antimicrobial resistance (AMR) is a chronic issue of our Westernized society, mainly because of the uncontrolled and improper use of antimicrobials. The coronavirus disease 2019 (COVID-19) pandemic has triggered and expanded AMR diffusion all over the world, and its clinical and therapeutic features have changed. Thus, we aimed to review evidence from the literature on the definition and causative agents of AMR in the frame of the COVID-19 post-pandemic era. Methods: We conducted a search on PubMed and Medline for original articles, reviews, meta-analyses, and case series using the following keywords, their acronyms, and their associations: antibiotics, antimicrobial resistance, severe acute respiratory syndrome coronavirus 2 (SARS-CoV2), COVID-19 pandemic, personal protective equipment. Results: AMR had a significant rise in incidence both in in-hospital and outpatient populations (ranging from 5 up to 50%) worldwide, but with a variegated profile according to the germ and microorganism considered. Not only bacteria but also fungi have developed more frequent and diffuse AMR. These findings are explained by the increased use and misuse of antibiotics and preventive measures during the first waves of the SARS-CoV2 pandemic, especially in hospitalized patients. Subsequently, the reduction in and end of the lockdown and the use of personal protective equipment have allowed for the indiscriminate circulation of resistant microorganisms from low-income countries to the rest of the world with the emergence of new multi- and polyresistant organisms. However, there is not a clear association between COVID-19 and AMR changes in the post-pandemic period. Conclusions: AMR in some microorganisms has significantly increased and changed its characteristics during and after the end of the pandemic phase of COVID-19. An integrated supranational monitoring approach to this challenge is warranted in the years to come. In detail, a rational, personalized, and regulated use of antibiotics and antimicrobials is needed.

Background and Objectives: Inflammatory bowel disease (IBD) is a condition characterized by chronic intestinal inflammation. We can identify two major forms: Crohn’s disease (CD) and ulcerative colitis (UC). One of the extraintestinal manifestations of IBD is nonalcoholic fatty liver disease (NAFLD). IBD and NAFLD share common pathogenetic mechanisms. Ultrasound (US) examination is the most commonly used imaging method for the diagnosis of NAFLD. This cross-sectional observational retrospective study aimed to evaluate the US prevalence of NAFLD in IBD patients and their clinical features. Materials and Methods: A total of 143 patients with IBD underwent hepatic US and were divided into two different groups according to the presence or absence of NAFLD. Subsequently, new exclusion criteria for dysmetabolic comorbidities (defined as plus) were applied. Results: The US prevalence of NAFLD was 23% (21% in CD and 24% in UC, respectively). Most IBD–NAFLD patients were male and older and showed significantly higher values for body mass index, waist circumference, disease duration, and age at onset than those without NAFLD. IBD–NAFLD patients showed a significantly higher percentage of stenosing phenotype and left-side colitis. Regarding metabolic features, IBD–NAFLD patients showed a significantly higher percentage of hypertension and IBD plus dysmetabolic criteria. Also, higher values of alanine aminotransferase and triglycerides and lower levels of high-density lipoproteins are reported in these patients. Conclusions: We suggest performing liver US screening in subjects affected by IBD to detect NAFLD earlier. Also, patients with NAFLD present several metabolic comorbidities that would fall within the new definition of metabolic-associated fatty liver disease. Finally, we encourage larger longitudinal studies, including healthy controls, to provide further confirmation of our preliminary data.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has generated an unprecedented challenge for healthcare systems worldwide. Currently, the scientific community wonders if liver injury in patients suffering from severe forms is a direct consequence of the virus or secondary manifestations of systemic inflammation. The liver plays an essential role in the development of the inflammatory storm typical of this disease, and its involvement is associated with worse clinical outcomes and a higher risk of morbidity and mortality from Coronavirus disease 2019 (COVID-19). Methods: Ten patients suffering from severe COVID-19 disease who died between January 2020 and December 2021 were included in the present analysis. These subjects underwent a post mortem examination with a focused evaluation of the hepatic injury. Also, several laboratory parameters have been evaluated, with a primary focus on prothrombin time, partial thromboplastin time, fibrinogen, antithrombin III, and D-dimers to detect coagulative changes. Results: The main cause of death was represented by pulmonary thromboembolism events (50%). The analysis of coagulation laboratory parameters and liver biomarkers revealed a statistically significant rise in aPTT and ALP, and a decrease in albumin, when comparing the blood value at admission and death. We also found high levels of D-dimers in most of the subjects at the time of hospitalization. Interestingly, the post mortem analysis of the liver showed ample morphologic variability, with several disease features. In detail, the liver histology revealed the following: the presence of a variable degree of micro- and macrovacuolar steatosis, inflammation (also, hepato-cholangitis), and variable fibrosis. Of mention, we were also able to detect organized fibrinous material. Conclusions: Our results indicate that in subjects with a severe form of COVID-19, liver disease is related to changes in coagulative and fibrinolytic pathways. In particular, we noted low fibrinogen levels and high D-dimer levels with histological liver findings. Our data suggest that fibrinogen and D-dimers may be used as prognostic markers to detect the severity of liver disease in patients with COVID-19. Finally, we underline the crucial role of coagulation balance in subjects with severe forms of COVID-19.

Combating antimicrobial resistance (AMR) requires comprehensive efforts, such as screening to identify patients colonized by multidrug-resistant microorganisms (MDROs). The primary purpose of this study was to estimate the AMR pattern of methicillin-resistant Staphylococcus aureus (MRSA) isolated from nasal surveillance swabs and MDROs isolated from pharyngeal and rectal surveillance swabs in patients attending a teaching hospital. Data were sought retrospectively, from 1 January 2017 to 31 December 2021, from the records produced by the hospital microbiology laboratory. Duplicate isolates, defined as additional isolates of the same microorganism with identical antibiograms, were excluded. Among Staphylococcus aureus isolates from nasal swabs, 18.2% were oxacillin-resistant. Among Gram-negative bacteria, 39.8% of Klebsiella pneumoniae and 83.5% of Acinetobacter baumannii isolates were carbapenem-resistant. Resistance to three antibiotic categories was high among Acinetobacter baumannii (85.8%) and Klebsiella pneumoniae (42.4%). The present data highlight a high prevalence of MDRO colonization among patients admitted to the hospital and suggest that screening for MDROs could be an important tool for infection control purposes, especially in geographical areas where limiting the spread of MDROs is crucial. The results also underline the importance of active surveillance, especially for carbapenem-resistant, Gram-negative bacteria in reducing their transmission, especially in high-risk units.

In the age of the smart city, things like the Internet of Things (IoT) and big data analytics are making big changes to the way traditional structural health monitoring (SHM) is done. Also, the capacity, flexibility, and robustness of artificial intelligence (AI) techniques for solving complex real-world problems have led to an increasing interest in applying these methods to SHM systems of infrastructures in recent years. Therefore, an analytical evaluation of recent advancements in SHM for infrastructures appears to be important. The bridge is one of the significant transportation infrastructures where existing environmental and destructive variables can have a negative impact on the structure’s life and health. The SHM system for bridges in different stages of their life cycle, such as construction, development, management, and maintenance, is seen as a complementary part of intelligent transportation systems (ITS). The main goal of this study is to look at how AI can be used to improve the current state of the art in data-driven SHM systems for bridges, including conceptual frameworks, advantages, and challenges, as well as existing approaches. This article presents an overview of the role of AI in data-driven SHM systems for bridges in the future. Finally, some potential research possibilities in AI-assisted SHM are also emphasized and detailed.

Background and Objectives: Hepatocellular carcinoma (HCC) is the leading cause of liver cancer worldwide and has a high mortality rate. Its incidence has increased due to metabolic-associated liver disease (MAFLD) epidemics. Liver transplantation and surgery remain the most resolute measures. Despite the optimistic use of multi-kinase inhibitors, namely sorafenib, the co-existence of chronic liver disease made the response rate low in these patients. Immune checkpoint inhibitors (ICIs) have become a promising hope for certain advanced solid tumors and, also, for advanced HCC. Unfortunately, a large cohort of patients with HCC fail to respond to immunotherapy. Materials and Methods: We conducted a narrative search on the main medical databases for original articles, reviews, meta-analyses, randomized clinical trials, and case series using the following keywords and acronyms and their associations: hepatocellular carcinoma, immunotherapy, checkpoint inhibitors, gut microbiota, and fecal microbiota transplantation. Results: ICIs are a promising and sufficiently safe treatment option for HCC. In detail, they have significantly improved survival and prognosis in these patients vs. sorafenib. Although there are several highlighted mechanisms of resistance, the gut microbiota signature can be used both as a response biomarker and as an effect enhancer. Practically, probiotic dose-finding and fecal microbiota transplantation are the weapons that can be used to increase ICI’s treatment-response-reducing resistance mechanisms. Conclusion: Immunotherapy has been a significant step-up in HCC treatment, and gut microbiota modulation is an effective liaison to increase its efficacy.

Liver transplantation (LT) is the treatment of choice for patients with cirrhosis, decompensated disease, acute liver failure, and hepatocellular carcinoma (HCC). In 3–25% of cases, an alarming problem is acute and chronic cellular rejection after LT, and this event can lead to the need for new transplantation or the death of the patient. On the other hand, gut microbiota is involved in several mechanisms sustaining the model of the “gut–liver axis”. These include modulation of the immune response, which is altered in case of gut dysbiosis, possibly resulting in acute graft rejection. Some studies have evaluated the composition of the gut microbiota in cirrhotic patients before and after LT, but few of them have assessed its impact on liver rejection. This review underlines the changes in gut microbiota composition before and after liver transplantation, hypothesizing possible immune mechanisms linking dysbiosis to transplantation rejection. Evaluation of changes in the gut microbiota composition in these patients is therefore essential in order to monitor the success of LT and eventually adopt appropriate preventive measures.

Metabolic-dysfunction-associated fatty liver disease (MAFLD) is the recent nomenclature designation that associates the condition of non-alcoholic fatty liver disease (NAFLD) with metabolic dysfunction. Its diagnosis has been debated in the recent period and is generally associated with a diagnosis of steatosis and at least one pathologic condition among overweight/obesity, type 2 diabetes mellitus, and metabolic dysregulation. Its pathogenesis is defined by a “multiple-hit” model and is associated with alteration or dysbiosis of the gut microbiota. The pathogenic role of dysbiosis of the gut microbiota has been investigated in many diseases, including obesity, type 2 diabetes mellitus, and NAFLD. However, only a few works correlate it with MAFLD, although common pathogenetic links to these diseases are suspected. This review underlines the most recurrent changes in the gut microbiota of patients with MAFLD, while also evidencing possible pathogenetic links.

Orthopedic and trauma device-related infections (ODRI) due to high virulence microorganisms are a devastating complication after orthopedic surgery. Coagulase-negative Staphylococci (CoNS) are mainly involved but commensal bacteria, located in human mucous membranes, are emerging pathogens in ODRI. Currently, bacterial culture is the gold standard for ODRI but the diagnostic process remains time consuming and laborious. We evaluated a combination of microbiological approaches in the diagnosis of emerging pathogens involved in ODRI. We analyzed two synovial fluids, five tissue samples and five surgical wound swabs from two different patients with ODRI, attending the Department of Orthopedic and Trauma Surgery of Mater Domini Teaching Hospital, Catanzaro, Italy. Identification was carried out with a combination of microbiological approaches (culture, mass spectrometry and 16s rRNA gene sequencing). We demonstrated the importance of a combination of microbiological approaches for the diagnosis of emerging pathogens in ODRI, because the low number of cases in the literature makes it very difficult to formulate guidelines for the management of patients.

The challenges of urban administration are growing, as the population, automobiles, and cities rise. Making cities smarter is thus one of the most effective solutions to urban issues. A key feature of the “smart cities” of today is that they use cutting-edge technology in their infrastructure and services. With strategic planning, the smart city utilizes its resources in the most efficient manner. With reduced expenses and enhanced infrastructure, smart cities provide their residents with more and better services. One of these important urban services that can be very helpful in managing cities is structural health monitoring (SHM). By combining leading new technologies like the Internet of Things (IoT) with structural health monitoring, important urban infrastructure can last longer and work better. A thorough examination of recent advances in SHM for infrastructure is thus warranted. Bridges are one of the most important parts of a city’s infrastructure, and their building, development, and proper maintenance are some of the most important aspects of managing a city. The main goal of this study is to look at how artificial intelligence (AI) and some technologies, like drone technology and 3D printers, could be used to improve the current state of the art in SHM systems for bridges, including conceptual frameworks, benefits and problems, and existing methods. An outline of the role AI and other technologies will play in SHM systems of bridges in the future was provided in this study. Some novel technology-aided research opportunities are also highlighted, explained, and discussed.

Road safety assessment is one of the most important parts of road transport safety management. When road transportation networks are managed safely, they improve the quality of life for citizens and the economy as a whole. On the one hand, there are many factors that affect road safety. On the other hand, this issue is a dynamic problem, which means that it is always changing. So, there is a dire need for a thorough evaluation of road safety to deal with complex and uncertain problems. For this purpose, two machine learning methods called “feature selection algorithms” are used. These algorithms include a combination of artificial neural network (ANN) with the particle swarm optimization (PSO) algorithm and the differential evolution (DE) algorithm. In this study, two data sets with 202 and 564 accident cases from cities and rural areas in southern Italy are investigated and analyzed based on several factors that affect transportation safety, such as light conditions, weekday, type of accident, location, speed limit, average speed, and annual average daily traffic. When the performance and results of the two models were compared, the results showed that the two models made the same choices. In rural areas, the type of accident and the location were chosen as the highest and lowest priorities, respectively. According to the results, useful suggestions regarding the improvement of road safety on urban and rural roads were provided. The average speed and location were considered the highest and lowest priorities in urban areas, respectively. Finally, there was not a big difference between the results of the two algorithms in terms of how well the algorithm models worked, but the proposed PSO model converged more quickly than the proposed DE model.

A methodology for calibrating and validating VISSIM simulation model is presented that allows to replicate the observed vehicles conflicts. A roundabout case study has been selected to test the usefulness of a combined approach of VISSIM simulation package and the surrogate safety assessment model (SSAM) for providing reliable estimates of traffic conflicts. Safety performance has been assessed from the field by video-recording vehicle interactions at the roundabout, and then expressed in terms of time to collision (TTC) values.The proposed calibration procedure has been performed by a multistage methodology involving microscopic drivers' car following behavior parameters to enhance the correlation between observed and simulated queue lengths at the roundabout's entries. The calibration procedure is based on a statistical screening of inputs leading to a linear expression relating significant parameters to the queue length. The best estimates of the model's parameters have been determined using a genetic algorithm technique.The spatial distribution of the rear-end conflicts and the TTC values determined by SSAM have been finally compared with the observed ones to analyze the capability of the model of replicating rear-end conflicts.The results suggest to this calibration procedure impacts positively on the estimate of the safety performance measures obtained through the simulation processes.Notwithstanding the good results in the evaluation of the model's accuracy, the simulation seems to fail in reproducing the traffic phenomena linked to unusual driving behavior, and therefore it is not able to replicate forced drivers' maneuvers that can lead to a conflict situation. Keywords: Simulation, Safety performance, Unmanned aerial vehicle, Roundabouts, Traffic conflicts

The COVID-19 pandemic may have had an effect on antimicrobial resistance. We compared the prevalence of ESKAPE multidrug-resistant (MDR) bacterial infections in COVID-19 affected/unaffected patients admitted to intensive care units (ICU) or infectious disease units at the “Mater Domini” University Hospital of Catanzaro between 1 March 2020 and 31 July 2021. Moreover, an analysis of MDR rates in ICU comparing the pre-pandemic period with the pandemic period was performed, and the possible consequence on in-hospital mortality was explored. One hundred and eighty-four ESKAPE isolates were analyzed from 362 SARS-CoV-2 positive and 199 negative patients. In total, 116 out of 171 Gram-negative isolates were classified as MDR, and a higher frequency was observed in COVID-19 compared with non-COVID-19 patients (74.2% vs. 60.3%; p = 0.052). A higher rate of MDR ESKAPE bacteria was observed in COVID-19 patients admitted to the ICU compared with COVID-19 unaffected patients admitted to the same ward in 2019 (88% vs. 80.4%; p = 0.186). Acinetobacter baumannii was the main pathogen in COVID-19 patients (58.7%), where it was the most frequent cause of bloodstream infection with the highest mortality rate (68.7%). Increase in MDR appeared to be associated with COVID-19 but only in the ICU setting. Acinetobacter baumannii was associated with the risk of death, indicating the importance of implementing infection control measures urgently.

With the increasing population of cities, expanding roads as one of the essential urban infrastructures is a necessary task; therefore, adverse effects such as increased fuel consumption, pollution, noise, and road accidents are inevitable. One of the most efficient ways to mitigate congestion-related adverse effects is to introduce effective intelligent transportation systems (ITS), using advanced technologies and mobile communication protocols to make roads smarter and reduce negative impacts such as improvement in fuel consumption and pollution, and reduction of road accidents, which leads to improving quality of life. Smart roads might play a growing role in the improved safety of road transportation networks. This study aims to evaluate and rank the potential smartification measures for the road network in Calabria, in southern Italy, with sustainable development goals. For this purpose, some potential smartification measures were selected. Experts in the field were consulted using an advanced procedure: four criteria were considered for evaluating these smartification measures. The Integrated fuzzy decision support system (FDSS), namely the fuzzy Delphi analytic hierarchy process (FDAHP) with the fuzzy technique for order performance by similarity to ideal solution (FTOPSIS) were used for evaluating and ranking the potential smartification measures. The results demonstrated that the repetition of signals in the vehicle has the highest rank, and photovoltaic systems spread along the road axis has the lowest rank to use as smartification measures in the roads of the case study.

The evaluation of road safety is a critical issue having to be conducted for successful safety management in road transport systems, whereas safety management is considered in road transportation systems as a challenging task according to the dynamic of this issue and the presence of a large number of effective parameters on road safety. Therefore, the evaluation and analysis of important contributing factors affecting the number of vehicles involved in crashes play a key role in increasing the efficiency of road safety. For this purpose, in this research work, two machine learning algorithms, including the group method of data handling (GMDH)-type neural network and a combination of support vector machine (SVM) and the grasshopper optimization algorithm (GOA), are employed. Hence, the number of vehicles involved in an accident is considered to be the output, and the seven factors affecting transport safety, including Daylight (DL), Weekday (W), Type of accident (TA), Location (L), Speed limit (SL), Average speed (AS), and Annual average daily traffic (AADT) of rural roads in Cosenza, southern Italy, are selected as the inputs. In this study, 564 data sets from rural areas were investigated, and the relevant, effective parameters were measured. In the next stage, several models were developed to investigate the parameters affecting the safety management of road transportation in rural areas. The results obtained demonstrated that the “Type of accident” has the highest level and “Location” has the lowest importance in the investigated rural area. Finally, although the results of both algorithms were the same, the GOA-SVM model showed a better degree of accuracy and robustness than the GMDH model.

This paper reviews the state of the art in traffic signal control methods that are based on data coming from onboard smartphones or connected vehicles. The review of the state of the art is carried out by applying analytical scientometric tools (topic visualization, co-citation analysis to establish influential journals and references, country analysis based on coauthorship, trending-topics analysis carried out by overlay visualization). The introduction of autonomous and connected vehicles will allow city management organizations to introduce new intersection management systems that rely on real-time positional data coming from instrumented vehicles. Traditional vehicles also could benefit from these new technologies by profiting from better-regulated intersections. This paper using a scientometric approach frames all the scientific contributions aimed at the field of traffic signal methods and experiments based on connected vehicles and floating car data. The applied scientometric approach reveals trending ideas and concepts and identifies the relevant documents that can be consulted in order for scientists and professionals to develop further this field with the implementation of new traffic signal control systems that can “give the green light” to drivers.

Speed has been identified for a long time as a key risk factor in road traffic: inappropriate speeds contribute to a relevant part of traffic accidents. Many literature studies have focused on the relationship between speed and accident risk. Starting from this consideration this paper investigates traffic accident risk by analysing the travelling speeds recorded by real tests on the road. A survey was conducted to collect experimental speed values in a real context. A specific road segment, belonging to an Italian rural two-lane road, was repeatedly run by 27 drivers in order to collect the instantaneous speed values for each trajectory. Smartphone-equipped vehicles were used to record continuous speed data. The recorded data were used to calculate: the average speed, 50th and 85th percentile speed for each geometric element of the analysed road segment. The main result of the research is the classification of car users’ driving behaviour based on the speed values. By using the above mentioned ranges of speed, the classification provides three types of driving behaviour: safe, unsafe, and safe but potentially dangerous. It was found that only four drivers feature “safe” behaviour, driving in a safe manner on most of the road elements. However, the major part of drivers, even if they feature safe behaviour, could be dangerous for other drivers because they drive at very low speeds.

This paper presents a methodology aimed to acquire traffic flow data through the employment of unmanned aerial vehicles (UAVs). The study is focused on the determination of driving behavior parameters of road users and on the reconstruction of traffic flow Origin/Destination matrix. The methodology integrates UAV flights with video image processing technique, and the capability of geographic information systems, to represent spatiotemporal phenomena. In particular, analyzing different intersections, the attention of the authors is focused on users’ gap acceptance in a naturalistic drivers’ behavior condition (drivers are not influenced by the presence of instruments and operators on the roadway) and on the reconstruction of vehicle paths. Drivers’ level of aggressiveness is determined by understanding how drivers decide that a gap is crossable and, consequently, how their behavior is critical in relation to a moving stream of traffic with serious road safety implications. The results of these experiments highlight the usefulness of the UAVs technology, that combined with video processing technique allows the capture of real traffic conditions with a good level of accuracy.

This paper presents a methodology for tracking moving vehicles that integrates Unmanned Aerial Vehicles with video processing techniques. The authors investigated the usefulness of Unmanned Aerial Vehicles to capture reliable individual vehicle data by using GPS technology as a benchmark. A video processing algorithm for vehicles trajectory acquisition is introduced. The algorithm is based on OpenCV libraries. In order to assess the accuracy of the proposed video processing algorithm an instrumented vehicle was equipped with a high precision GPS. The video capture experiments were performed in two case studies. From the field, about 24,000 positioning data were acquired for the analysis. The results of these experiments highlight the versatility of the Unmanned Aerial Vehicles technology combined with video processing technique in monitoring real traffic data.

Speed has been identified for a long time as a key risk factor in road traffic. Inappropriate speeds contribute to a relevant part of road crashes, and then to the mortality and disabilities resulting from them. Starting from this consideration this paper investigates road safety by analysing operating speed, which is the 85th percentile speed. Particularly, two regression models are proposed to predict operating speeds for different road elements related to specific road conditions. The case study is represented by a two-lane rural highway. Smartphone-equipped vehicles were used to evaluate the operating speed for each element of the analysed road segment. Continuous speed data were recorded by the vehicles driven by users with different driving behaviours. Since the lack of safety is often linked to an inconsistency roadway geometric design, we effected a preliminary quantitative design consistency evaluation that confirmed the need of having accurate experimental measures of operating speed or appropriate models for predicting it. We propose two types of operating speed models: one for estimating speed profiles for horizontal curves, and the other one for tangents. According to both models, operating speed is predicted by the combination of an independent variable representing a geometric characteristic (curve radius for the curves and length for the tangent elements) and an independent variable relating to the speed, and specifically the operating speed of the previous road element. The models show a good predictive capability, and can be considered as a useful tool for operators and technicians for road management. First published online: 01 Nov 2015

Recently, many researchers have employed a microsimulation technique to study the chain of interactions among vehicles, which generates an accident occurrence in some circumstances. This new approach to studying road safety is named traffic conflict technique. The aim of this paper is to assess how the microscopic simulation is a useful tool to identify potentially unsafe vehicle interactions and how high-risk locations identified by a microsimulation technique are similar to the ones identified by using historical accident data. Results show that high-risk locations identified by the simulation framework are superimposable to those identified by using the historical accident database. In particular, the statistical analysis employed based on Pearson’s correlation demonstrates a significative correspondence between a risk rate defined with simulation and an accident rate determined by the observed accidents dataset.

New technologies such as “connected„ and “autonomous„ vehicles are going to change the future of traffic signal control and management and possibly will introduce new traffic signal systems that will be based on floating car data (FCD). The use of floating car data to regulate traffic signal systems, in real time, has the potential for an increased sustainability of transportation in terms of energy efficiency, traffic safety and environmental issues. However, research has never explored how not “connected„ vehicles would benefit by the implementation of such systems. This paper explores the use of floating car data to regulate traffic signal systems in real-time in a single intersection and in terms of cooperative-competitive paradigm between “connected„ vehicles and conventional vehicles. In a dedicated laboratory, developed for testing regulation algorithms, results show that “invisible vehicles„ for the system (which are not “connected„) in most simulated cases also benefit when real time traffic signal settings based on floating car data are introduced. Moreover, the study estimates the energy and air quality impacts of a single intersection signal regulation by evaluating fuel consumption and pollutant emissions. Specifically, the study demonstrates that significant improvements in air quality are possible with the introduction of FCD regulated traffic signals.

The European Commission has recently promoted research programs aimed at finding solutions to the ever more compelling problem of air pollution from road vehicles and has also indicated a better sustainability among the possible impacts of co-operative Intelligent Transportation Systems. In fact, many practical solutions can be developed that allow drivers and management to optimise resources and to contain costs and the emissions of pollutants by applying communication systems between vehicles (Vehicle-to-Vehicle – V2V) and between vehicles and infrastructure (Vehicle-to-Infrastructure – V2I). Along this mainstream this paper present a co-operative system which offer drivers the ability to manage their consumption and driving style, suggesting corrections to the usually adopted behaviour. The new contribution of this paper is both the co-operative approach between drivers to achieve a common goal of a better common energy consumption strategy and a methodology to estimate fuel consumption just by using Satellite data obtained from a simple smartphone. Since the fuel consumption has to be evaluated with regards to the specific vehicle type the system is based also on crowdsourcing of the specific vehicle consumption performances. The paper describes a system that gathers data on fuel consumption from the co-operating drivers that can build together the data set necessary to the system itself once they accept this paper paradigm: crowd sourced co-operation for a smarter and more sustainable transport system.

Background: Inflammatory bowel disease (IBD) is characterized by persistent inflammation and is often associated with metabolic dysfunction-associated steatotic liver disease (MASLD). IBD patients are at risk of developing MASLD due to shared risk factors such as gut dysbiosis and systemic inflammation. The new MASLD nomenclature emphasizes the link between liver steatosis and cardiometabolic comorbidities. However, the prevalence of MASLD in IBD patients remains poorly explored. The main aim of this cross-sectional study is to assess the prevalence of ultrasound (US) and the clinical features of MASLD in patients with IBDs. Materials and Methods: We conducted a retrospective study enrolling 272 Italian IBD patients attending Renato Dulbecco Teaching Hospital in a period between 1 January 2021 and 31 December 2023. MASLD was diagnosed based on the presence of liver steatosis with cardiometabolic risk factors, using established guidelines. Demographic, clinical, and laboratory data were collected and analyzed. Statistical significance was determined at a p-value < 0.05. Results: Of the 272 IBD patients, 6% had non-alcoholic fatty liver disease (NAFLD), while 18% had MASLD. Patients with IBD-MASLD were significantly older, had higher body mass index, waist circumference, and triglyceride levels, and were more likely to have type 2 diabetes mellitus and hypertension compared to those with IBD-NAFLD. IBD-MASLD patients also showed higher disease activity scores and required more frequent surgical interventions. Bivariate logistic regression revealed triglyceride levels as a significant predictor of MASLD in IBD patients. Conclusions: MASLD is more prevalent in IBD patients, highlighting the importance of early detection of liver steatosis in this at-risk population. The association between MASLD and cardiometabolic risk factors underscores the need for a multidisciplinary approach to manage these patients effectively. Further studies in larger cohorts are necessary to confirm these findings and explore the pathophysiological mechanisms involved. [ABSTRACT FROM AUTHOR]

Superior mesenteric artery (SMA) syndrome or Wilkie's syndrome is a vascular compression disorder that causes the abnormal compression of the third portion of the duodenum by the SMA. It has a low incidence rate, which is higher in young women, and is rarely associated with the Nutcracker phenomenon: a condition of the compression of the left renal vein between the SMA and the aorta, which manifests as pain in the left flank and pelvis. Here, we report on the case of a 54-year-old woman with a history of repeated episodes of abdominal pain caused by the Nutcracker syndrome and Wilkie's syndrome. [ABSTRACT FROM AUTHOR]

Background: Clostridioides difficile infection (CDI) is a significant cause of mortality, especially in healthcare environments. Reliable biomarkers that can accurately predict mortality in CDI patients are yet to be evaluated. Our study aims to evaluate the accuracy of several inflammatory biomarkers and hemogram-derived ratios in predicting mortality in CDI patients, such as the neutrophil-to-lymphocyte ratio (NLR), the systemic immune-inflammation index (SII), the platelet-to-neutrophil ratio (PNR), the derived neutrophil-to-lymphocyte ratio (dNLR), C-reactive protein (CRP), the platelet-to-lymphocyte ratio (PLR), and procalcitonin (PCT). Results: NLR showed a sensitivity of 72.5% and a specificity of 58.42% with an area under curve (AUC) = 0.652. SII had a sensitivity of 77.5%, a specificity of 54.74%, and an AUC = 0.64. PNR, neutrophils, dNLR, and lymphocytes had lower AUCs which ranged from 0.595 to 0.616, with varied sensitivity and specificity. CRP, leukocytes, and platelets showed modest predictive values with AUCs below 0.6. PCT had a sensitivity of 100%, a low specificity of 7.41%, and an AUC = 0.528. Methods: We conducted a retrospective analysis of CDI patients from two different hospital settings in Italy and Romania during the COVID-19 pandemic, from 1 January 2020 to 5 May 2023. Statistical analyses included t-tests, Wilcoxon rank-sum tests, χ2 tests, and multivariate logistic regression to identify predictors of mortality. ROC analysis assessed the accuracy of biomarkers and hemogram-derived ratios. A p value < 0.05 was considered significant. Conclusions: Neutrophils, dNLR, NLR, SII, and PNR are valuable biomarkers for predicting mortality in CDI patients. Understanding these predictors can improve risk stratification and clinical outcomes for CDI patients. [ABSTRACT FROM AUTHOR]