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Abstract Background Dual-energy subtraction (DES) imaging is critical in contrast-enhanced mammography (CEM), as the recombination of low-energy (LE) and high-energy (HE) images produces contrast enhancement while reducing anatomical noise. The study's purpose was to compare the performance of the DES algorithm among three different CEM systems using a commercial phantom. Methods A CIRS Model 022 phantom, designed for CEM, was acquired using all available automatic exposure modes (AECs) with three CEM systems from three different manufacturers (CEM1, CEM2, and CEM3). Three studies were acquired for each system/AEC mode to measure both radiation dose and image quality metrics, including estimation of measurement error. The mean glandular dose (MGD) calculated over the three acquisitions was used as the dosimetry index, while contrast-to-noise ratio (CNR) was obtained from LE and HE images and DES images and used as an image quality metric. Results On average, the CNR of LE images of CEM1 was 2.3 times higher than that of CEM2 and 2.7 times higher than that of CEM3. For HE images, the CNR of CEM1 was 2.7 and 3.5 times higher than that of CEM2 and CEM3, respectively. The CNR remained predominantly higher for CEM1 even when measured from DES images, followed by CEM2 and then CEM3. CEM1 delivered the lowest MGD (2.34 ± 0.03 mGy), followed by CEM3 (2.53 ± 0.02 mGy) in default AEC mode, and CEM2 (3.50 ± 0.05 mGy). The doses of CEM2 and CEM3 increased by 49.6% and 8.0% compared with CEM1, respectively. Conclusion One system outperformed others in DES algorithms, providing higher CNR at lower doses. Relevance statement This phantom study highlighted the variability in performance among the DES algorithms used by different CEM systems, showing that these differences can be translated in terms of variations in contrast enhancement and radiation dose. Key Points DES images, obtained by recombining LE and HE images, have a major role in CEM. Differences in radiation dose among CEM systems were between 8.0% and 49.6%. One DES algorithm achieved superior technical performance, providing higher CNR values at a lower radiation dose. Graphical Abstract

Abstract Background Automatic exposure control (AEC) plays a crucial role in mammography by determining the exposure conditions needed to achieve specific image quality based on the absorption characteristics of compressed breasts. This study aimed to characterize the behavior of AEC for digital mammography (DM), digital breast tomosynthesis (DBT), and low-energy (LE) and high-energy (HE) acquisitions used in contrast-enhanced mammography (CEM) for three mammography systems from two manufacturers. Methods Using phantoms simulating various breast thicknesses, 363 studies were acquired using all available AEC modes 165 DM, 132 DBT, and 66 LE-CEM and HE-CEM. AEC behaviors were compared across systems and modalities to assess the impact of different technical components and manufacturers’ strategies on the resulting mean glandular doses (MGDs) and image quality metrics such as contrast-to-noise ratio (CNR). Results For all systems and modalities, AEC increased MGD for increasing phantom thicknesses and decreased CNR. The median MGD values (interquartile ranges) were 1.135 mGy (0.772–1.668) for DM, 1.257 mGy (0.971–1.863) for DBT, 1.280 mGy (0.937–1.878) for LE-CEM, and 0.630 mGy (0.397–0.713) for HE-CEM. Medians CNRs were 14.2 (7.8–20.2) for DM, 4.91 (2.58–7.20) for a single projection in DBT, 11.9 (8.0–18.2) for LE-CEM, and 5.2 (3.6–9.2) for HE-CEM. AECs showed high repeatability, with variations lower than 5% for all modes in DM, DBT, and CEM. Conclusions The study revealed substantial differences in AEC behavior between systems, modalities, and AEC modes, influenced by technical components and manufacturers’ strategies, with potential implications in radiation dose and image quality in clinical settings. Relevance statement The study emphasized the central role of automatic exposure control in DM, DBT, and CEM acquisitions and the great variability in dose and image quality among manufacturers and between modalities. Caution is needed when generalizing conclusions about differences across mammography modalities. Key points • AEC plays a crucial role in DM, DBT, and CEM. • AEC determines the “optimal” exposure conditions needed to achieve specific image quality. • The study revealed substantial differences in AEC behavior, influenced by differences in technical components and strategies. Graphical Abstract

Despite the fact that fibromyalgia, a widespread disease of the musculoskeletal system, has no specific treatment, patients have shown improvement after pharmacological intervention. Pregabalin has demonstrated efficacy; however, its adverse effects may reduce treatment adherence. In this context, neuromodulatory techniques such as transcranial direct current stimulation (tDCS) may be employed as a complementary pain-relieving method. Consequently, the purpose of this study was to evaluate the effect of pregabalin and tDCS treatments on the behavioral and biomarker parameters of rats submitted to a fibromyalgia-like model.

Background: Multiple-session home-based self-applied transcranial direct current stimulation (M-HB-self-applied-tDCS) has previously been found to effectively reduce chronic pain and enhance cognitive function. However, the effectiveness of this method for disordered eating behavior still needs to be studied. Objective: This study aimed to assess whether 20 sessions of M-HB-self-applied-tDCS, administered over four weeks to either the left dorsolateral prefrontal cortex (L-DLPFC) or primary motor cortex (M1), could improve various aspects of eating behavior, anthropometric measures, and adherence. Methods: We randomly assigned 102 fibromyalgia patients between the ages of 30 and 65 to one of four tDCS groups: L-DLPFC (anodal-(a)-tDCS, n = 34; sham-(s)-tDCS, n = 17) or M1 (a-tDCS, n = 34; s-tDCS, n = 17). Patients self-administered 20-min tDCS sessions daily with 2 mA under remote supervision following in-person training. Results: Generalized linear models revealed significant effects of M-HB-self-applied-tDCS compared to s-tDCS on uncontrolled eating (UE) (Wald χ2 = 5.62; df = 1; P = 0.018; effect size, ES = 0.55), and food craving (Wald χ2 = 5.62; df = 1; P = 0.018; ES = 0.57). Regarding fibromyalgia symptoms, we found a differentiated impact of a-tDCS on M1 compared to DLPFC in reducing food cravings. Additionally, M-HB-a-tDCS significantly reduced emotional eating and waist size. In contrast, M1 stimulation was more effective in improving fibromyalgia symptoms. The global adherence rate was high, at 88.94%. Conclusion: These findings demonstrate that M-HB-self-applied-tDCS is a suitable approach for reducing uncontrolled and emotional eating, with greater efficacy in L-DLPFC. Furthermore, these results revealed the influence of fibromyalgia symptoms on M-HB-self-applied-tDCS's, with M1 being particularly effective in mitigating food cravings and reducing fibromyalgia symptoms.

Abstract The printed circuit board (PCB) plays a critical role in any electronic product, and its manufacturing quality assurance is responsible for substantially impacting the final product’s price. Hence, research and development (R&D) for better inspection methods have been an internationally growing subject. Regarding the multiple PCB inspection paradigms, the visual investigation of these boards represents the majority of modern techniques. Thus, the present survey collects the most impacting studies over the last 25 years, highlighting their operation strategies and how they evolved until recently, including introducing artificial intelligence (AI) approaches to increase their overall performance and lower operating costs. Furthermore, this work calls attention to the importance of public PCB datasets to contribute to developing such methods and provide benchmarking references. Finally, the most recent challenges in the field are described and discussed.

This study introduces a sophisticated neural network structure for segmenting breast tumors. It achieves this by combining a pretrained Vision Transformer (ViT) model with a UNet framework. The UNet architecture, commonly employed for biomedical image segmentation, is further enhanced with depthwise separable convolutional blocks to decrease computational complexity and parameter count, resulting in better efficiency and less overfitting. The ViT, renowned for its robust feature extraction capabilities utilizing self-attention processes, efficiently captures the overall context within images, surpassing the performance of conventional convolutional networks. By using a pretrained ViT as the encoder in our UNet model, we take advantage of its extensive feature representations acquired from extensive datasets, resulting in a major enhancement in the model’s ability to generalize and train efficiently. The suggested model has exceptional performance in segmenting breast cancers from medical images, highlighting the advantages of integrating transformer-based encoders with efficient UNet topologies. This hybrid methodology emphasizes the capabilities of transformers in the field of medical image processing and establishes a new standard for accuracy and efficiency in activities related to tumor segmentation.

Background: The Brief Measure of Preoperative Emotional Stress (B-MEPS) is a suitable screening tool for Preoperative Emotional Stress (PES). However, personalized decision-making demands practical interpretation of the refined version of B-MEPS. Thus, we propose and validate cut-off points on the B-MEPS to classify PES. Also, we assessed if the cut-off points screened preoperative maladaptive psychological features and predicted postoperative opioid use. Methods: This observational study comprises samples of two other primary studies, with 1009 and 233 individuals, respectively. The latent class analysis derived emotional stress subgroups using B-MEPS items. We compared membership with the B-MEPS score through the Youden index. Concurrent criterion validity of the cut-off points was performed with the severity of preoperative depressive symptoms, pain catastrophizing, central sensitization, and sleep quality. Predictive criterion validity was performed with opioid use after surgery. Results: We chose a model with three classes labeled mild, moderate, and severe. The Youden index points -0.1663 and 0.7614 of the B-MEPS score classify individuals, in the severe class, with a sensitivity of 85.7% (80.1%–90.3%) and specificity of 93.5% (91.5–95.1%). The cut-off points of the B-MEPS score have satisfactory concurrent and predictive criterion validity. Conclusions: These findings showed that the preoperative emotional stress index on the B-MEPS offers suitable sensitivity and specificity for discriminating the severity of preoperative psychological stress. They provide a simple tool to identify patients prone to severe PES related to maladaptive psychological features, which might influence the perception of pain and analgesic opioid use in the postoperative period.

This article aims to present the results of a national, cross-sectional, voluntary, online survey on the presence and roles of associations of breast cancer patients and volunteers in Italian specialist breast centres. The survey was developed according to standard methods. The questionnaire was pre-tested by a random sample of three breast centres, loaded onto the SurveyMonkey platform, and piloted by one volunteer breast centre. The breast centre clinical leads were invited to participate via email. A link to the online instrument was provided. No financial incentives were offered. The results were reported using standard descriptive statistics. The response rate was 82/128 (65%). Members of associations were routinely present in 70% Italian breast centres. Breast centres most often reporting their presence were those certified by the European Society of Breast Cancer Specialists. Patient support (reception and information, listening, identification of needs, and psychological support) was the primary area where associations were reported to offer services. The magnitude of this phenomenon warrants a study to investigate the impact of the activities of associations on the quality of life of patients and on the cost–benefit ratio of the service, and the modes of their interactions with the nursing staff and the medical staff.

This study aimed to investigate clinical and physiological predictors of brain oscillatory activity in patients with fibromyalgia (FM), assessing resting-state power, event-related desynchronization (ERD), and event-related synchronization (ERS) during tasks. We performed a cross-sectional analysis, including clinical and neurophysiological data from 78 subjects with FM. Multivariate regression models were built to explore predictors of electroencephalography bands. Our findings show a negative correlation between beta oscillations and pain intensity; fibromyalgia duration is positively associated with increased oscillatory power at low frequencies and in the beta band; ERS oscillations in the theta and alpha bands seem to be correlated with better symptoms of FM; fatigue has a signature in the alpha band—a positive relationship in resting-state and a negative relationship in ERS oscillations. Specific neural signatures lead to potential clusters of neural adaptation, in which beta oscillatory activity in the resting state represents a more adaptive activity when pain levels are low and stimulus-evoked oscillations at lower frequencies are likely brain compensatory mechanisms. These neurophysiological changes may help to understand the impact of long-term chronic pain in the central nervous system and the descending inhibitory system in fibromyalgia subjects.