XXXI INTERREGIONAL CONGRESS OF THE SICILIAN AND CALABRIA REGIONAL SECTIONS (CALABRO-SICULO) OF THE ITALIAN SOCIETY OF HYGIENE, PREVENTIVE MEDICINE AND PUBLIC HEALTH (S.It.I.)

The abstract book of Oral Communications of Young Public Health Professionals at the XXXI Interregional Congress of the Sicilian and Calabria Regional Sections of the Italian Society of Hygiene, Preventive Medicine and Public Health (S.It.I.), that was held in Vibo Valentia (Italy) from the 14th to the 16th of September 2023

Healthcare workers and prevention of hepatitis C virus transmission: exploring knowledge, attitudes and evidence-based practices in hemodialysis units in Italy

Abstract Background Evidence exists regarding the full prevention of HCV transmission to hemodialysis patients by implementing universal precaution. However, little information is available regarding the frequency with which hospitals have adopted evidence-based practices for preventing HCV infection among hemodialysis patients. A cross-sectional survey has been conducted among nurses in Calabria region (Italy) in order to acquire information about the level of knowledge, the attitudes and the frequencies of evidence-based practices that prevent hospital transmission of HCV. Methods All 37 hemodialysis units (HDU) of Calabria were included in the study and all nurses were invited to participate in the study and to fill in a self-administered questionnaire. Results 90% of the nurses working in HDU participated in the study. Correct answers about HCV pattern of transmission ranged from 73.7% to 99.3% and were significantly higher in respondents who knew that isolation of HCV-infected patients is not recommended and among those who knew that previous bloodstream infections should be included in medical record and among nurses with fewer years of practice. Most correctly thought that evidence-based infection control measures provide adequate protection against transmission of bloodborne pathogens among healthcare workers. Positive attitude was significantly higher among more knowledgeable nurses. Self-reporting of appropriate handwashing procedures were significantly more likely in nurses who were aware that transmission of bloodborne pathogens among healthcare workers may be prevented through adoption of evidence-based practices and with a correct knowledge about HCV transmission patterns. Conclusions Behavior changes should be aimed at abandoning outdated practices and adopting and maintaining evidence-based practices. Initiatives focused at enabling and reinforcing adherence to effective prevention practices among nurses in HDU are strongly needed. </jats:sec

Wound Care Self-Efficacy Assessment of Italian Registered Nurses and Wound Care Education in Italian Nursing Education System: A Cross-Sectional Study

Wounds are a major public health challenge for nurses, and poor wound care has important implications for patients and health care systems. The aim of this study is to assess the Italian registered nurses’ (RNs) perception in the area of wound care, regarding their knowledge, tasks of care delivery, wound management, values, and attitudes, exploring also the previous specific education received during nursing education. An observational online web-based survey was used to assess learning goals and content for wound care education in undergraduate nursing education and the skills and level of self-efficacy in this area during clinical practice. The data were collected between April and May 2022. A total of 210 RNs were interviewed and divided into five national geographic areas. Northwestern RNs showed a better education about the wound care area during university courses: the rate of RNs that did not receive any training in the wound care area was lower than in other Italian geographical areas. Southern RNs presented a better knowledge about factors that expose the wound to becoming chronic, wound drains care, and the ability to assess diabetic foot. This study showed that, in Italy, education in wound care among nursing students is relatively poor, and many skills are achieved during an RN’s career in an empirical way

A novel framework for MR image segmentation and quantification by using MedGA.

BACKGROUND AND OBJECTIVES: Image segmentation represents one of the most challenging issues in medical image analysis to distinguish among different adjacent tissues in a body part. In this context, appropriate image pre-processing tools can improve the result accuracy achieved by computer-assisted segmentation methods. Taking into consideration images with a bimodal intensity distribution, image binarization can be used to classify the input pictorial data into two classes, given a threshold intensity value. Unfortunately, adaptive thresholding techniques for two-class segmentation work properly only for images characterized by bimodal histograms. We aim at overcoming these limitations and automatically determining a suitable optimal threshold for bimodal Magnetic Resonance (MR) images, by designing an intelligent image analysis framework tailored to effectively assist the physicians during their decision-making tasks. METHODS: In this work, we present a novel evolutionary framework for image enhancement, automatic global thresholding, and segmentation, which is here applied to different clinical scenarios involving bimodal MR image analysis: (i) uterine fibroid segmentation in MR guided Focused Ultrasound Surgery, and (ii) brain metastatic cancer segmentation in neuro-radiosurgery therapy. Our framework exploits MedGA as a pre-processing stage. MedGA is an image enhancement method based on Genetic Algorithms that improves the threshold selection, obtained by the efficient Iterative Optimal Threshold Selection algorithm, between the underlying sub-distributions in a nearly bimodal histogram. RESULTS: The results achieved by the proposed evolutionary framework were quantitatively evaluated, showing that the use of MedGA as a pre-processing stage outperforms the conventional image enhancement methods (i.e., histogram equalization, bi-histogram equalization, Gamma transformation, and sigmoid transformation), in terms of both MR image enhancement and segmentation evaluation metrics. CONCLUSIONS: Thanks to this framework, MR image segmentation accuracy is considerably increased, allowing for measurement repeatability in clinical workflows. The proposed computational solution could be well-suited for other clinical contexts requiring MR image analysis and segmentation, aiming at providing useful insights for differential diagnosis and prognosis

Influenza vaccination coverage among medical residents: An Italian multicenter survey

Although influenza vaccination is recognized to be safe and effective, recent studies have confirmed that immunization coverage among health care workers remain generally low, especially among medical residents (MRs). Aim of the present multicenter study was to investigate attitudes and determinants associated with acceptance of influenza vaccination among Italian MRs. A survey was performed in 2012 on MRs attending post-graduate schools of 18 Italian Universities. Each participant was interviewed via an anonymous, self-administered, web-based questionnaire including questions on attitudes regarding influenza vaccination. A total of 2506 MRs were recruited in the survey and 299 (11.9%) of these stated they had accepted influenza vaccination in 2011-2012 season. Vaccinated MRs were older (P = 0.006), working in clinical settings (P = 0.048), and vaccinated in the 2 previous seasons (P < 0.001 in both seasons). Moreover, MRs who had recommended influenza vaccination to their patients were significantly more compliant with influenza vaccination uptake in 2011-2012 season (P < 0.001). "To avoid spreading influenza among patients" was recognized as the main reason for accepting vaccination by less than 15% of vaccinated MRs. Italian MRs seem to have a very low compliance with influenza vaccination and they seem to accept influenza vaccination as a habit that is unrelated to professional and ethical responsibility. Otherwise, residents who refuse vaccination in the previous seasons usually maintain their behaviors. Promoting correct attitudes and good practice in order to improve the influenza immunization rates of MRs could represent a decisive goal for increasing immunization coverage among health care workers of the future. © 2014 Landes Bioscience

ACDC: Automated Cell Detection and Counting for Time-Lapse Fluorescence Microscopy.

Advances in microscopy imaging technologies have enabled the visualization of live-cell dynamic processes using time-lapse microscopy imaging. However, modern methods exhibit several limitations related to the training phases and to time constraints, hindering their application in the laboratory practice. In this work, we present a novel method, named Automated Cell Detection and Counting (ACDC), designed for activity detection of fluorescent labeled cell nuclei in time-lapse microscopy. ACDC overcomes the limitations of the literature methods, by first applying bilateral filtering on the original image to smooth the input cell images while preserving edge sharpness, and then by exploiting the watershed transform and morphological filtering. Moreover, ACDC represents a feasible solution for the laboratory practice, as it can leverage multi-core architectures in computer clusters to efficiently handle large-scale imaging datasets. Indeed, our Parent-Workers implementation of ACDC allows to obtain up to a 3.7× speed-up compared to the sequential counterpart. ACDC was tested on two distinct cell imaging datasets to assess its accuracy and effectiveness on images with different characteristics. We achieved an accurate cell-count and nuclei segmentation without relying on large-scale annotated datasets, a result confirmed by the average Dice Similarity Coefficients of 76.84 and 88.64 and the Pearson coefficients of 0.99 and 0.96, calculated against the manual cell counting, on the two tested datasets

Knowledge, attitude and practices of pediatricians regarding the prevention of oral diseases in Italy

BACKGROUND: Pediatricians are in an ideal position to advise families about the prevention and management of oral diseases in children. The objective of the study was to determine knowledge, attitude, and practices regarding the prevention of oral diseases among pediatricians in Italy. METHODS: A systematic random sample of 1000 pediatricians received a questionnaire on socio-demographic and practice characteristics; knowledge on risk factors; attitude and practices towards the prevention of oral diseases. RESULTS: A total of 507 pediatricians participated. More than half knew the main risk factors for oral diseases and this knowledge was higher in primary care pediatricians (p = 0.007), in those with a higher number of hours worked per week (p = 0.012), and who believed that oral diseases may be prevented (p = 0.017). Pediatricians with higher knowledge about the main risk factors (p = 0.006) believe that they have an important role in preventing oral diseases and that they can perform an oral examination. Almost all (89%) prescribed fluoride supplements and those younger (p = 0.016), with a higher number of patients seen in workday (p = 0.001), with longer practice activity (p = 0.004), those who believe that fluoride is effective in preventing caries (p < 0.0001), and who learned about prevention from scientific sources (p = 0.002) were more likely to prescribe fluoride. One-fourth and 40.6% provides and recommends a dental visit once a year and primary care pediatricians (p = 0.014) and those who believed that routine visit is important in preventing oral diseases (p < 0.0001) were more likely to recommend a dental visit once a year. CONCLUSION: The results showed a lack of knowledge among pediatricians although almost all believed that they had an important responsibility in preventing oral diseases and provided an oral examination

USE-Net: Incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets

Prostate cancer is the most common malignant tumors in men but prostate Magnetic Resonance Imaging (MRI) analysis remains challenging. Besides whole prostate gland segmentation, the capability to differentiate between the blurry boundary of the Central Gland (CG) and Peripheral Zone (PZ) can lead to differential diagnosis, since the frequency and severity of tumors differ in these regions. To tackle the prostate zonal segmentation task, we propose a novel Convolutional Neural Network (CNN), called USE-Net, which incorporates Squeeze-and-Excitation (SE) blocks into U-Net, i.e., one of the most effective CNNs in biomedical image segmentation. Especially, the SE blocks are added after every Encoder (Enc USE-Net) or Encoder-Decoder block (Enc-Dec USE-Net). This study evaluates the generalization ability of CNN-based architectures on three T2-weighted MRI datasets, each one consisting of a different number of patients and heterogeneous image characteristics, collected by different institutions. The following mixed scheme is used for training/testing: (i) training on either each individual dataset or multiple prostate MRI datasets and (ii) testing on all three datasets with all possible training/testing combinations. USE-Net is compared against three state-of-the-art CNN-based architectures (i.e., U-Net, pix2pix, and Mixed-Scale Dense Network), along with a semi-automatic continuous max-flow model. The results show that training on the union of the datasets generally outperforms training on each dataset separately, allowing for both intra-/cross-dataset generalization. Enc USE-Net shows good overall generalization under any training condition, while Enc-Dec USE-Net remarkably outperforms the other methods when trained on all datasets. These findings reveal that the SE blocks’ adaptive feature recalibration provides excellent cross-dataset generalization when testing is performed on samples of the datasets used during training. Therefore, we should consider multi-dataset training and SE blocks together as mutually indispensable methods to draw out each other's full potential. In conclusion, adaptive mechanisms (e.g., feature recalibration) may be a valuable solution in medical imaging applications involving multi-institutional settings