Thirty-two patients affected by SSTIs including DFIs were enrolled between 2013 and 2014. Superficial swab was obtained before and after cleansing with sterile saline, and after ultrasonic debridement; deep tissue biopsy was obtained from ulcer base. Samples were diluted with 1 mL of saline, serial 10-fold dilutions to 10 were made and 50 μL of each dilution was plated onto appropriate media. Bacteria were identified by Vitek II system. Microbial load was expressed as CFU/mL. Statistical analysis was performed by χ2. Incidence of Gram positives was higher than Gram negatives (S. aureus and P. aeruginosa being the most frequent); concordance (same bacteria isolated before and after debridement) never exceeded 60%. Ultrasonic debridement significantly reduced bacterial load or even suppressed bacterial growth. While reliability of superficial swab is poor for microbiological diagnosis of SSTIs, swabbing after ultrasonic debridement and biopsy of the ulcer base may be equally reliable.

Serum uric acid (sUA) has been associated with cardiovascular risk. Although the recent mechanistic hypothesis poses the basis for the association between sUA and left ventricular mass index (LVMi), the issue remains poorly investigated in a clinical setup. Through a retrospective analysis of the database of the departmental Hypertension Clinic of University Hospital of Salerno Medical School, we identified 177 essential hypertensives (age 60.3 ± 13.3 years; 85 men), free from uric acid-modulating medications and severe chronic kidney disease, and whose sUA values, anthropometric, clinical, and echocardiographic data were available. In the studied cohort, the average duration of hypertension was 8.4 ± 7.1 years. LVMi associated with classical determinants, such as age, blood pressure, and kidney function, although after multivariate correction, only age remained significant. Also, sUA correlated positively with LVMi, as well as body size, metabolism, and kidney function. In a multivariate analysis, sUA confirmed the independent association with LVMi. Also, levels of sUA >5.6 mg/dl are associated with larger cardiac size. We confirmed our data in a replicate analysis performed in a larger population (1,379 hypertensives) from an independent clinic. Our results demonstrate that sUA increases with LVMi, and a cutoff of 5.6 mg/dl predict larger LV sizes. Our data suggest that hyperuricemia might help to stratify the risk of larger cardiac size in hypertensives.

A 51-year-old woman was admitted to the emergency unit with diffuse headache, visus reduction, and paraesthesias of the trigeminal area and the left hand. Three days after admission she showed shaking chills, vomiting, and sudden onset of fever (39·4°C). Blood cultures were performed soon after fever onset. Fever persisted for the whole day, decreasing slowly after 12 hours. No empirical antibiotic treatment was started in order to better define the diagnosis. Fever completely disappeared the day after. Two blood cultures for aerobes were positive for Chryseobacterium indologenes. The patient was discharged with the diagnosis of transient bacteraemia and transferred to the neurology unit for further investigations. C. indologenes infections are described in 31 studies with a total of 171 cases (pneumonia and bacteraemia being the most frequent). Our case is the first report of transient bacteraemia caused by C. indologenes in an immunocompetent, non-elderly patient without needing medical devices.

ObjectivesSurgical infections represent an increasingly important problem for the National Health System. In this study we retrospectively evaluated the bacterial epidemiology and antimicrobial susceptibility of the microorganisms concerned as well as the utilization of antibiotics in the General and Emergency Surgery wards of a large teaching hospital in southern Italy in the period 2011–2013.MethodsData concerning non-duplicate bacterial isolates and antimicrobial susceptibility were retrieved from the Vitek 2 database. The pharmacy provided data about the consumption of antibiotics in the above reported wards. Chi-square or Fisher’s exact test were used.ResultsIn all, 94 Gram-negative were isolated in 2011, 77 in 2012, and 125 in 2013, Escherichia coli, Acinetobacter baumannii and Pseudomonas aeruginosa always being the most frequently isolated microorganisms. A. baumannii showed high rates of resistance to carbapenems (with values of 100% in 2011 and 2012) and low rates of resistance to tigecycline, colistin and amikacin. In the same years, there were respectively 105, 93, and 165 Gram-positive isolated. The rate of MRSA isolates ranged from 66% to 75% during the study period.ConclusionsOur results show no significant increase in antimicrobial resistance over the period in question, and a higher rate of both MRSA isolates and resistance to carbapenems in A. baumannii compared with other European data.

Arterial hypertension (AH) is a progressive issue that grows in importance with the increased average age of the world population. The potential role of artificial intelligence (AI) in its prevention and treatment is firmly recognized. Indeed, AI application allows personalized medicine and tailored treatment for each patient. Specifically, this article reviews the benefits of AI in AH management, pointing out diagnostic and therapeutic improvements without ignoring the limitations of this innovative scientific approach. Consequently, we conducted a detailed search on AI applications in AH: the articles (quantitative and qualitative) reviewed in this paper were obtained by searching journal databases such as PubMed and subject-specific professional websites, including Google Scholar. The search terms included artificial intelligence, artificial neural network, deep learning, machine learning, big data, arterial hypertension, blood pressure, blood pressure measurement, cardiovascular disease, and personalized medicine. Specifically, AI-based systems could help continuously monitor BP using wearable technologies; in particular, BP can be estimated from a photoplethysmograph (PPG) signal obtained from a smartphone or a smartwatch using DL. Furthermore, thanks to ML algorithms, it is possible to identify new hypertension genes for the early diagnosis of AH and the prevention of complications. Moreover, integrating AI with omics-based technologies will lead to the definition of the trajectory of the hypertensive patient and the use of the most appropriate drug. However, AI is not free from technical issues and biases, such as over/underfitting, the “black-box” nature of many ML algorithms, and patient data privacy. In conclusion, AI-based systems will change clinical practice for AH by identifying patient trajectories for new, personalized care plans and predicting patients’ risks and necessary therapy adjustments due to changes in disease progression and/or therapy response.