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Macrolide-resistant Mycoplasma pneumoniae (MR-MP) infections cause upper and lower respiratory tract infections in both children and adults, and are characterized by a longer duration of symptoms. Here, we undertook a systematic review of studies on MR-MP in Europe. The review meets PRISMA guidelines. The PubMed, Scopus, and Science Direct databases were searched using suitable keywords to identify relevant studies published from 2010 to 2021; 21 studies were included. Overall, a low level of MR-MP spread was reported in Europe. MR-MP spread increased during epidemic waves registered in Europe, particularly in Italy and Scotland, where the highest MR-MP infection rates were registered during the 2010–2011 epidemic. By contrast, no MR-MP infections were reported in Finland and the Netherlands. Continued monitoring of MR-MP in Europe is needed to maintain the low rates of infection. Moreover, a coordinated and structured pan-European surveillance program adequate for public health surveillance is advisable, with the purpose of containing the spread of antimicrobial resistance.

Background. In emergency hospital settings, rapid diagnosis and isolation of SARS-CoV-2 patients are required. The aim of the study was to evaluate the performance of an antigen chemiluminescence enzymatic immunoassay (CLEIA) and compare it with that of Real-time Reverse transcription-Polymerase Chain Reaction (RT-qPCR), the gold standard assay, to assess its suitability as a rapid diagnostic method for managing patients in the emergency department (ED). Methods. Consecutive patients with no previous history of SARS-CoV-2 infection attending the ED of the Policlinico Hospital of Bari between 23rd October and 4th November 2020 were enrolled. Clinical and demographic data were collected for all patients. Nasopharyngeal swabs collected on admission were subjected both to molecular (RT-qPCR) and antigen (CLEIA) tests for SARS-CoV-2. The performance of the CLEIA antigen test was analyzed using R Studio software and Microsoft Excel. Receiver operating characteristics were also performed. Results. A total of 911 patients were enrolled, of whom 469 (51.5%) were male. Of the whole cohort, 23.7% tested positive for SARS-CoV-2 by RT-qPCR and 24.5% by CLEIA. The overall concordance rate was 96.8%. The sensitivity, specificity, positive predictive value, and negative predictive value of the antigen test were 94.9% (95% CI, 91.9–97.0), 97.4% (95% CI, 96.5–98.1), 91.9% (95% CI, 89.0–94.0), and 98.4% (95% CI, 97.4–99.1), respectively. The area under the curve (AUC) was 0.99. The kappa coefficient was 0.91. The overall positive and negative likelihood ratios were 37 (95% CI 23-58) and 0.05 (95% CI, 0.03–0.09), respectively. Conclusions. Data analysis demonstrated that the antigen test showed very good accuracy for discriminating SARS-CoV-2-infected patients from negative participants. The CLEIA is suitable for rapid clinical diagnosis of patients in hospital settings, particularly in EDs with a high prevalence of symptomatic patients and where a rapid turnaround time is critical. Timely and accurate testing for SARS-CoV-2 plays a crucial role in limiting the spread of the virus.

Differences in the demographic and clinical characteristics of patients infected with the Alpha and Delta SARS-CoV-2 variants of concern in a large region of Southern Italy were assessed. Two cohorts of positive patients were compared. The Alpha group consisted of 11,135 subjects diagnosed between 21 March and 21 April 2021, and the Delta group consisted of 499 positive subjects diagnosed between 21 July and 21 August 2021. A descriptive and statistical analysis of the demographic and clinical characteristics of the two groups was performed. The proportion of patients with mild and moderate infections was significantly higher in the Delta than in the Alpha group (p < 0.001). In fully vaccinated patients, the proportion of symptomatic individuals was significantly higher in the Delta than in the Alpha group. The Delta group showed odds ratios of 3.08 (95% CI, 2.55–3.72) for symptomatic infection and 2.66 (95% CI, 1.76–3.94) for hospitalization. Improving COVID-19 vaccination rates is a priority, since infection with the SARS-CoV-2 Delta variant has a significant impact on patient outcomes. Additional targeted prevention strategies such as social distancing, the use of masks in indoor settings irrespective of vaccination status, and the use of a sanitary passport could be crucial to contain further spread of SARS-CoV-2 infection.

Since its initial detection, the SARS-CoV-2 Omicron sublineage BA.2 has been spreading rapidly worldwide. The aims of this study were to describe the first 284 patients infected with the Omicron BA.2 variant of concern (VOC) in the Apulia region of southern Italy and to assess the differences in the demographic and clinical characteristics of patients infected with the SARS-CoV-2 BA.1 and BA.2 variants. The demographic characteristics of patients, as well as information about symptoms, vaccinations and hospitalizations for COVID-19, were collected. A subset of samples from patients infected with the BA.2 variant was subjected to whole-genome sequencing. The characteristics of the first 284 patients infected with Omicron BA.2 and the first 175 patients infected with Omicron BA.1 were compared. The proportion of patients infected with the BA.2 variant rapidly increased, from 0.5% during the third week of 2022 to 29.6% during the tenth week of 2022. Ten isolates (out of 34 BA.2 isolates) contain the substitutional mutation, H78K in ORF3a, and four isolates include two mutations, A2909V in ORF1a and L140F in ORDF3a. Compared with patients infected with BA.1, those infected with BA.2 were more likely to be symptomatic and booster-vaccinated, and showed a shorter time from the last dose of vaccine to infection. The high transmissibility and immune-evasive properties of Omicron BA.2, which will become the leading SARS-CoV-2 VOC, suggest that short-term public health measures should not be discontinued in Italy.

Epidemiological and virological studies have revealed that SARS-CoV-2 variants of concern (VOCs) are emerging globally, including in Europe. The aim of this study was to evaluate the spread of B.1.1.7-lineage SARS-CoV-2 in southern Italy from December 2020–March 2021 through the detection of the S gene target failure (SGTF), which could be considered a robust proxy of VOC B.1.1.7. SGTF was assessed on 3075 samples from week 52/2020 to week 10/2021. A subset of positive samples identified in the Apulia region during the study period was subjected to whole-genome sequencing (WGS). A descriptive and statistical analysis of the demographic and clinical characteristics of cases according to SGTF status was performed. Overall, 20.2% of samples showed SGTF; 155 strains were confirmed as VOC 202012/01 by WGS. The proportion of SGTF-positive samples rapidly increased over time, reaching 69.2% in week 10/2021. SGTF-positive cases were more likely to be symptomatic and to result in hospitalization (p < 0.0001). Despite the implementation of large-scale non-pharmaceutical interventions (NPIs), such as the closure of schools and local lockdowns, a rapid spread of VOC 202012/01 was observed in southern Italy. Strengthened NPIs and rapid vaccine deployment, first among priority groups and then among the general population, are crucial both to contain the spread of VOC 202012/01 and to flatten the curve of the third wave.

At the beginning of the coronavirus-2019 (COVID-19) pandemic, Italy was one of the most affected countries in Europe. Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection is less frequent and less severe in children than in adults. This study analyzed the frequency of SARS-CoV-2 infection among all children aged <18 years in the Apulia region of southern Italy and the characteristics of the infected children. Clinical and demographic data were collected through the national platform for COVID-19 surveillance. Of the 166 infected children in the Apulia region, 104 (62.6%) were asymptomatic, 37 (22.3%) had mild infections, 22 (13.3%) had moderate infections, and 3 (1.8%) had severe infections. Only ten children (6.0%) were hospitalized, but none required intensive care support and none died. SARS-CoV-2 infection was transmitted mainly from parents or relatives to children. Because of school closure during the lockdown, infection was unlikely to have been transmitted among children. It is unclear whether school reopening would enhance virus spread, leading the Italian government to develop guidelines for safe school reopening. The actual role of children in virus transmission remains unclear. A sensitive surveillance system, prompt identification of cases, testing, and contact tracing will be key to reducing the further spread of infection.

A critical point in the management of the SARS-CoV-2 pandemic is the need to promptly identify the greatest number of infected people and to implement strict public health measures. In this study, the performance of a rapid serological test in a clinical setting was evaluated. Samples from 819 consecutive patients (with or without respiratory symptoms) admitted to a large Emergency Department were tested between 23 March and 21 April 2020. Patient samples were tested in a real-time PCR assay and a serological assay. In total, 148/819 patients (18.1%) tested positive for SARS-CoV-2 by real-time PCR. The serological test revealed that 70/819 patients (8.5%) had anti-SARS-CoV-2 IgM and/or IgG. The prevalence of anti-SARS-CoV-2 antibodies was significantly higher in patients with respiratory symptoms lasting for >7 days than in those with respiratory symptoms lasting for 0–7 days (p < 0.001). The serological assay had an overall sensitivity of 35.1% and an overall specificity of 97.3%. A high negative predictive value (96.7%) was reported for patients without respiratory symptoms. The results confirm that rapid serological assays alone are not sufficient for diagnosis of SARS-CoV-2 infection but can be incorporated into large-scale screening programs during periods in which the virus circulation is low.

Deep-learning models applied to contrast-enhanced mammography (CEM) are known to be highly sensitive to the input image representation. However, preprocessing is often treated as a secondary step and rarely analyzed as an independent design variable. In this work, we present a systematic engineering analysis of a deterministic, label-independent preprocessing pipeline for CEM images. The pipeline integrates intensity normalization, global histogram matching, local contrast enhancement, denoising, and anatomically constrained breast masking. Using a controlled experimental design, identical deep-learning architectures were trained under different input representations to isolate the impact of preprocessing on classification performance and stability. Across convolutional neural network architectures, anatomically constrained preprocessing consistently improves discrimination performance, reduces variability across cross-validation folds, and enhances training stability. Breast mask-based representations demonstrate substantial gains in AUROC and AUPRC compared to raw DICOM inputs. These findings highlight image preprocessing as a first-class engineering component in medical AI pipelines. Breast masking significantly improves robustness and generalization, independently of network architecture complexity. From a clinical perspective, improving model robustness and sensitivity to malignant lesions may contribute to more reliable AI-assisted decision support in contrast-enhanced mammography, particularly in settings characterized by acquisition variability and heterogeneous patient populations.

Background: This study investigates the impact of anatomically constrained preprocessing and deep learning architecture selection on benign versus malignant breast lesion classification in contrast-enhanced mammography (CEM), with the goal of improving robustness and clinical reliability across heterogeneous data sources. Methods: In this retrospective multicenter study, CEM images from 300 patients (314 lesions) were combined with 1003 publicly available CEM images, yielding a total of 1120 breast cases. Automatic breast segmentation was performed using the LIBRA framework to generate breast-mask images. Eleven deep learning models, including classical convolutional neural networks, attention-based networks, hybrid convolutional neural networks (CNNs), Transformer architectures, and mammography-specific models, were trained and evaluated using both original DICOM images and breast-mask inputs. Performance was assessed using accuracy, balanced accuracy, sensitivity, specificity, AUROC, and AUPRC on cross-validation and independent test sets. Hyperparameter optimization was conducted for the best-performing architecture. Results: Models trained on breast-mask images consistently outperformed those trained on original DICOM images across all architectures and metrics, with AUROC improvements ranging from +0.06 to +0.21. Among all models, ResNet50 trained on breast-mask images achieved the best performance (AUROC = 0.931; AUPRC = 0.933; balanced accuracy = 0.834), further improved after optimization (balanced accuracy = 0.886; sensitivity = 0.842; specificity = 0.930). Classical CNN architectures demonstrated performance comparable to or exceeding that of more complex hybrid CNN–Transformer models when anatomically focused preprocessing and rigorous optimization were applied. Conclusions: Anatomically constrained preprocessing through breast-mask segmentation substantially enhances deep learning performance and stability in CEM-based breast lesion classification. These findings indicate that input representation quality and training optimization are critical determinants of clinically relevant performance, often outweighing architectural complexity, and may support more reliable AI-assisted decision support in CEM workflows.