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The painting studied, featuring the Virgin contemplating the sleeping Child, is a well-known composition in the religious context, especially popular since the 16th century. The technique, oil on copper plate, and materials were examined applying a multimethodological non-invasive approach. The main goal was to quantitatively characterize the layer composition and thickness. To obtain them, X-ray fluorescence spectroscopy coupled with Monte Carlo simulation was used for the first time, to the best of our knowledge, for this kind of object. This technique was also integrated with more conventional photographic and spectroscopic techniques such as multiband imaging (MBI), Raman spectroscopy, external reflection Fourier transform infrared spectroscopy (ER FT-IR), and optical microscopy. The obtained data showed the use of a background based on lead white, a wide palette, which includes azurite, vermilion, lead white, and some ochers, and documented the conservation status. Although the investigations contended with the object’s complexity, the results demonstrate the efficacy of the XRF–Monte Carlo simulation approach in characterizing the materials constituting the artwork in a totally non-invasive way.

Background: Hashimoto’s thyroiditis (HT) is a common autoimmune thyroid disorder characterized by elevated anti-thyroid peroxidase (A-TPO) antibodies. HT frequently coexists with other autoimmune conditions, which are marked by organ-specific and non-organ-specific autoantibodies, reflecting a deregulated immune response. However, the burden and clinical significance of these circulating autoantibodies in adult patients with HT remains unclear. Methods: A cross-sectional study was conducted at the University Hospital “R. Dulbecco” in Catanzaro, Italy, from November 2023 to May 2024, involving 200 euthyroid adults. The study population comprised 100 A-TPO-positive HT patients and 100 A-TPO-negative controls, matched for age and sex. Laboratory assessments included thyroid function tests and detection of autoantibodies [e.g., antinuclear antibodies (ANA), anti-parietal cell antibodies (APCA), and anti-neutrophil cytoplasmic antibodies (ANCA)]. Cytokine profiles were also measured using sensitive chemiluminescent multi-array technology. Results: HT patients were predominantly female (77.0%) with a median age of 56 years. Compared to controls, HT patients had higher median thyroid stimulating hormone (TSH) levels (2.215 vs. 1.705 μIU/mL, p = 0.025). Circulating autoantibodies were more prevalent in the HT group, with higher rates of APCA positivity (16.3% vs. 4.1%, p = 0.008) and atypical ANCA positivity (27.3% vs. 10.2%, p = 0.003). This suggests an increased risk for autoimmune gastritis and systemic inflammation. Additionally, HT patients with positive atypical ANCA showed elevated inflammatory cytokines, particularly interleukin-1 alpha (IL-1α), in female patients (p = 0.035). Conclusions: HT is significantly associated with a higher prevalence of circulating autoantibodies, such as APCA and atypical ANCA, which may indicate a heightened risk for autoimmune gastritis and broader autoimmune involvement. Detecting these autoantibodies in HT patients could serve as markers for more severe autoimmune dysfunction. These findings emphasize the need for proactive screening, especially in older patients and those with elevated A-TPO levels. Further research is essential to better understand the clinical implications and develop targeted management strategies for these patients.

Abstract The observation of action seems to involve the generation of the internal representation of that same action in the observer, a process named motor resonance (MR). The objective of this study was to verify whether an experimental paradigm of action observation in a laboratory context could elicit cortical motor activation in 21 early Parkinson’s disease (PD) patients compared to 22 controls. Participants were instructed to simply observe (observation-only session) or to respond (Time-to-contact detection session) at the instant the agent performed a grasping action toward a graspable or ungraspable object. We used functional near-infrared spectroscopy with 20 channels on the motor and premotor brain areas and event-related desynchronization of alpha-mu rhythm. In both groups, response times were more accurate in graspable than ungraspable object trials, suggesting that motor resonance is present in PD patients. In the Time-to-contact detection session, the oxyhemoglobin levels and alpha-mu desynchronization prevailed in the graspable object trials rather than in the ungraspable ones. This study demonstrates the preservation of MR mechanisms in early PD patients. The action observation finalized to a consequent movement can activate cortical networks in patients with early PD, suggesting early rehabilitation interventions taking into account specific observation paradigms preceding motor production.

Background: Obesity constitutes a chronic, low-grade inflammatory status that predisposes people to the development of insulin resistance and cardiometabolic complications. Hypoxia, a main pathological feature of visceral fat in obese individuals, has been shown to affect the secretome of murine 3T3-L1 adipose cells, causing the upregulation of prothymosin-alpha (ProT-α), which is a protein with immunomodulatory functions that was originally found in the thymus. The aim of this case–control observational study was to measure the circulating levels of ProT-α in obese and lean individuals and determine whether such levels are correlated with inflammatory and metabolic parameters. Methods: Sixty-one obese patients (BMI ≥ 30 Kg/m2) and fifty-one age-matched, lean controls (BMI 18.5–24.9 Kg/m2) were recruited in the Endocrinology Unit (“Mater-Domini”) of the University Hospital of Catanzaro, Italy. The exclusion criteria included affliction with acute and systemic inflammatory states (i.e., leukocytosis), recent infectious diseases or vaccinations, obesity complications (i.e., type 2 diabetes mellitus and cardiovascular diseases), hepatic or renal failure, pregnancy and lactation, cancer, use of drugs or alcohol, and smoking. Apart from routine biochemical determinations, serum samples were screened for the presence of ProT-α using an ELISA method and for the presence of a panel of inflammatory cytokines and growth factors via a multiparametric chemiluminescence micro-array. Results: Between the age-matched groups, no statistically significant differences were shown in relation to fasting glucose, HbA1c, liver function tests, lipid profiles, circulating interleukins (IL)-1α, -1β, -2, -4, -8, and -10, MCP-1, TNF-α, VEGF and EGF. Instead, significantly higher median levels were observed in obese patients vs. lean controls with respect to fasting insulin levels (p < 0.001), a classic insulin resistance marker, and IL-6 (p = 0.004). In addition, ProT-α levels were significantly and considerably higher in obese patients compared to lean controls (median ProT-α, 600.0 vs. 411.5 pg/mL, p = 0.004) and showed a moderate to strong positive relationship with fasting insulin levels and selected cytokines (i.e., TNF-α and IL-8). Conclusions: An increase in circulating levels of ProT-α is linked with obesity and can be detected before any clinical cardiometabolic complications develop. ProT-α may represent a novel and sensitive biomarker for inflammation and insulin resistance in obese individuals.

In the field of neuroscience, brain–computer interfaces (BCIs) are used to connect the human brain with external devices, providing insights into the neural mechanisms underlying cognitive processes, including aesthetic perception. Non-invasive BCIs, such as EEG and fNIRS, are critical for studying central nervous system activity and understanding how individuals with cognitive deficits process and respond to aesthetic stimuli. This study assessed twenty participants who were divided into control and impaired aging (AI) groups based on MMSE scores. EEG and fNIRS were used to measure their neurophysiological responses to aesthetic stimuli that varied in pleasantness and dynamism. Significant differences were identified between the groups in P300 amplitude and late positive potential (LPP), with controls showing greater reactivity. AI subjects showed an increase in oxyhemoglobin in response to pleasurable stimuli, suggesting hemodynamic compensation. This study highlights the effectiveness of multimodal BCIs in identifying the neural basis of aesthetic appreciation and impaired aging. Despite its limitations, such as sample size and the subjective nature of aesthetic appreciation, this research lays the groundwork for cognitive rehabilitation tailored to aesthetic perception, improving the comprehension of cognitive disorders through integrated BCI methodologies.

Automatic vision-based inspection systems have played a key role in product quality assessment for decades through the segmentation, detection, and classification of defects. Historically, machine learning frameworks, based on hand-crafted feature extraction, selection, and validation, counted on a combined approach of parameterized image processing algorithms and explicated human knowledge. The outstanding performance of deep learning (DL) for vision systems, in automatically discovering a feature representation suitable for the corresponding task, has exponentially increased the number of scientific articles and commercial products aiming at industrial quality assessment. In such a context, this article reviews more than 220 relevant articles from the related literature published until February 2023, covering the recent consolidation and advances in the field of fully-automatic DL-based surface defects inspection systems, deployed in various industrial applications. The analyzed papers have been classified according to a bi-dimensional taxonomy, that considers both the specific defect recognition task and the employed learning paradigm. The dependency on large and high-quality labeled datasets and the different neural architectures employed to achieve an overall perception of both well-visible and subtle defects, through the supervision of fine or/and coarse data annotations have been assessed. The results of our analysis highlight a growing research interest in defect representation power enrichment, especially by transferring pre-trained layers to an optimized network and by explaining the network decisions to suggest trustworthy retention or rejection of the products being evaluated.

IntroductionSubtle cognitive dysfunction and mental fatigue are frequent after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, characterizing the so-called long COVID-19 syndrome. This study aimed to correlate cognitive, neurophysiological, and olfactory function in a group of subjects who experienced acute SARS-CoV-2 infection with persistent hyposmia at least 12 weeks before the observation.MethodsFor each participant (32 post-COVID-19 patients and 16 controls), electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) data were acquired using an integrated EEG–fNIRS system during the execution of a P300 odd-ball task and a Stroop test. The Sniffin' Sticks test was conducted to assess subjects' olfactory performance. The Montreal Cognitive Assessment (MoCA) and the Frontal Assessment Battery (FAB) were also administered.ResultsThe post-COVID-19 group consisted of 32 individuals (20 women and 12 men) with an average education level of 12.9 ± 3.12 years, while the control group consisted of 16 individuals (10 women and 6 men) with an average education level of 14.9 ± 3.2 years. There were no significant differences in gender (X2 = 0, p = 1) or age between the two groups (age 44.81 ± 13.9 vs. 36.62 ± 11.4, p = 0.058). We identified a lower concentration of oxyhemoglobin (p < 0.05) at the prefrontal cortical level in post-COVID-19 subjects during the execution of the Stroop task, as well as a reduction in the amplitude of the P3a response. Moreover, we found that post-COVID-19 subjects performed worst at the MoCA screening test (p = 0.001), Sniffin's Sticks test (p < 0.001), and Stroop task response latency test (p < 0.001).ConclusionsThis study showed that post-COVID-19 patients with persistent hyposmia present mild deficits in prefrontal function, even 4 months after the end of the infection. These deficits, although subtle, could have long-term implications for quality of life and cognitive wellbeing. It is essential to continue monitoring and evaluating these patients to better understand the extent and duration of cognitive impairments associated with long COVID-19.

Thromboembolic conditions are the most common cause of death in developed countries. Anticoagulant therapy is the treatment of choice, and heparinoids and warfarin are the most adopted drugs. Sulphated polysaccharides extracted from marine organisms have been demonstrated to be effective alternatives, blocking thrombus formation by inhibiting some factors involved in the coagulation cascade. In this study, four acidic glycan fractions from the marine sponge Sarcotragus spinosulus were purified by anion-exchange chromatography, and their anticoagulant properties were investigated through APTT and PT assays and compared with both standard glycosaminoglycans and holothurian sulphated polysaccharides. Moreover, their topographic localization was assessed through histological analysis, and their cytocompatibility was tested on a human fibroblast cell line. A positive correlation between the amount of acid glycans and the inhibitory effect towards both the intrinsic and extrinsic coagulation pathways was observed. The most effective anticoagulant activity was shown by a highly charged fraction, which accounted for almost half (about 40%) of the total hexuronate-containing polysaccharides. Its preliminary structural characterization, performed through infrared spectroscopy and nuclear magnetic resonance, suggested that it may consist of a fucosylated chondroitin sulphate, whose unique structure may be responsible for the anticoagulant activity reported herein for the first time.

Introduction: Differentiated thyroid carcinoma (DTC) is frequently found in conjunction with autoimmune thyroid disorders, particularly Hashimoto’s thyroiditis (HT). This study investigates the impact of coexisting HT on the persistence of an indeterminate response to therapy due to positive anti-thyroglobulin antibodies (AbTg), measured via competitive immunoassay, in a consecutive patient series from Calabria, Southern Italy. Methods: This retrospective longitudinal study analyzed 259 consecutive DTC patients managed at the Endocrinology Unit of Renato Dulbecco Hospital (Catanzaro, Italy) up to 2023. Patients with medullary and undifferentiated thyroid carcinoma, partial thyroidectomy, less than six months of post-operative monitoring, or missing clinical data were excluded. Demographic information, histological findings, initial tumor stage, and ATA risk category were collected. The response to therapy was assessed based on ATA guidelines. Results: Among the 259 patients, 29% had coexisting HT. Patients with HT exhibited distinct characteristics: a higher proportion of females (87.0% vs. 74.7%), a shorter post-operative monitoring duration (median 3 vs. 5 years), and a higher prevalence of papillary thyroid carcinoma (PTC) (97.4% vs. 86.3%). The tumor size, lymph node involvement, and distant metastasis were similar between the groups, with patients without HT having a higher incidence of extrathyroidal tumor extension. However, the initial TNM stage and ATA risk category did not differ significantly. At the six-month follow-up, HT patients showed a higher rate of indeterminate responses, primarily due to positive AbTg. After 12 months, the response categories aligned, with decreasing AbTg levels in the HT group. After 24 months, most patients with long-term follow-up demonstrated an excellent response to DTC therapy, irrespective of HT coexistence. Conclusions: While HT does not worsen DTC prognosis, it may result in indeterminate responses. AbTg measurements in the peri-operative period should be encouraged to facilitate post-operative monitoring, emphasizing the importance of using standardized assays. Further research in larger populations with extended follow-up is needed to comprehensively understand the HT-DTC relationship.

Background: The American Joint Commission on Cancer on Tumor Node Metastasis (AJCC/TNM) staging system provides adequate information on the risk of differentiated thyroid cancer (DTC)-specific mortality in totally thyroidectomized patients, but its role in predicting persistence and relapse of disease is uncertain. The relatively new 2015 American Thyroid Association (ATA) guidelines recommend stratifying patients at the time of DTC diagnosis with its own risk classification system, in order to identify those at high risk of residual or recurrent morbidity who may benefit from post-operative radioiodine (RAI) administration and/or need additional work-up. Methods: To verify the prevalence proportion of persistence or relapse of disease, a consecutive cohort of 152 patients with a diagnosis of DTC, subjected to total thyroidectomy (+/− post-operative RAI administration as per guidelines indication) and to neck ultrasonography (US), as well as biochemical surveillance for a minimum of 2 years at the Endocrinology Unit of Mater-Domini Hospital (Catanzaro, Italy), was enrolled. The prognostic role of the AJCC/TNM stage and ATA risk classification system was analyzed by logistic regression. Results: At a mean of 9 years after surgical treatment, DTC was found to persist or relapse in 19 (12.5%) participants. The initial risk for these outcomes, based on the ATA classification, was mostly low (53.9%) or intermediate (39.5%). AJCC/TNM stages were predominantly stage I or stage II. Despite a small representation in this cohort, high-risk patients according to the ATA classification had 8-fold higher odds of persistence or relapse of disease than those of low-risk participants, while controlling for potential risk modifiers, including age at DTC diagnosis, male gender, and post-operative RAI administration (p = 0.008). In contrast, the AJCC/TNM stage was not associated with the disease status at the last follow-up visit (p = 0.068 for the 7th Edition; p = 0.165 for the 8th Edition). Furthermore, low-risk participants subjected to post-operative RAI administration had the same probability of persistence or relapse of DTC when compared to those who had undergone total thyroidectomy only. Conclusions: There is a need for the endocrine community to revise the current work-up of DTC. The initial ATA risk classification is a reliable tool for predicting the persistence or relapse of disease in long-term surveillance.

BackgroundArtificial neural networks (ANNs) and logistic regression (LR) are the models of chosen in many medical data classification tasks. Several published articles were based on summarizing the differences and similarities of these models from a technical point of view and critically assessing the quality of the models. The aim of this study was to compare ANN and LR the statistical techniques to predict gastrointestinal cancer in an elderly cohort in Southern Italy (ONCONUT study).MethodIn 1992, ONCONUT was started with the aim of evaluating the relationship between diet and cancer development in a Southern Italian elderly population. Patients with gastrointestinal cancer (ICD-10 from 150.0 to 159.9) were included in the study (n = 3,545).ResultsThis cohort was used to train and test the ANN and LR. LR was evaluated separately for macro- and micronutrients, and the accuracy was evaluated based on true positives and true negatives versus the total (97.15%). Then, ANN was trained and the accuracy was evaluated (96.61% for macronutrients and 97.06% for micronutrients). To further investigate the classification capabilities of ANN, k-fold cross-validation and genetic algorithm (GA) were used after balancing the dataset among classes.ConclusionsBoth LR and ANN had high accuracy and similar performance. Both models had the potential to be used as decision clinical support integrated into clinical practice, because in many circumstances, the use of a simple LR model was likely to be adequate for real-world needs, but in others in which there were large amounts of data, the application of advanced analytic tools such as ANNs could be indicated, and the GA optimizer needed to optimize the accuracy of ANN.

Background and study aims—Albuminuria, defined as an enhanced urine albumin/creatinine ratio (ACR) on a spot sample, is a validated biomarker of glomerular damage. However, it cannot always detect early renal failures in patients with type 2 diabetes (T2D), thus prompting the search for more sensitive and specific parameters. Herein, we investigated the differential role of plasma and urine neutrophil-gelatinase-associated lipocalin (NGALp,—NGALu) for the detection of diabetic kidney disease (DKD). Methods—Traditional glomerular (serum creatinine, cystatin C, ACR) damage biomarkers were evaluated in 84 patients with T2D and in 21 metabolically healthy controls. Diabetic patients were stratified into four groups based on T2D duration (less or more than 5 years) and presence and severity of DKD (early- or advanced-stage), as defined by the ACR and estimated glomerular filtration rate (eGFR). NGALp and NGALu were determined by ELISA methodology and compared among groups. Results—There was no difference in NGALp and NGALu levels between the metabolically healthy individuals and the age-matched, newly diagnosed diabetic patients in the absence of DKD. However, in contrast to NGALu, NGALp was found to be substantially increased in patients with long-standing diabetes without biochemical evidence of DKD, closely mirroring the modest, but still accelerated, decline in the eGFR typical of this chronic dysmetabolic condition, and remained overexpressed throughout the stages of DKD progression. Increased NGALu levels were, instead, rather specific in patients with biochemical evidence of DKD (i.e., marked by increased albuminuria), regardless of T2D duration. Spearman’s correlation and regression analyses showed that patient age and T2D duration could exert a strong positive impact exclusively on NGALp concentrations (ρ = 0.419, p < 0.001 for age; ρ = 0.581, p < 0.001 for T2D), and none on NGALu. Furthermore, receiver operating characteristic (ROC) analysis showed the best performance of NGALp compared to NGALu for the detection of DKD (AUC = 0.817 for NGALp, AUC = 0.711 for NGALu). Conclusions—Our data suggest a different pathophysiological and predictive role for urine and plasma NGAL in the context of T2D and DKD.

Background: Despite patients undergoing chronic hemodialysis (HD) being notoriously prone to adverse cardiovascular (CV) events, risk prediction in this population remains challenging. miRNA 122-5p, a short, non-coding RNA predominantly involved in lipid and carbohydrate metabolism, has recently been related to the onset and progression of CV disease. Methods: We run a pilot, multicenter, longitudinal, observational study to evaluate the clinical significance and prognostic usefulness of circulating miRNA 122-5p in a multicentric cohort of 74 individuals on maintenance HD. Results: Patients displayed lower circulating miRNA 122-5p as compared to healthy controls (p = 0.004). At correlation analyses, ALT (β = 0.333; p = 0.02), E/e’ (β = 0.265; p = 0.02) and CRP (β = −0.219; p = 0.041) were independent predictors of miRNA 122-5p levels. During a median follow-up of 22 months (range of 1–24), 30 subjects (40.5%) experienced a composite endpoint of all-cause mortality and fatal/non-fatal CV events. Baseline circulating miRNA 122-5p was higher in these subjects (p = 0.01) and it predicted a significantly higher risk of endpoint occurrence (Kaplan–Meier crude HR 3.192; 95% CI 1.529–6.663; p = 0.002; Cox regression adjusted HR 1.115; 95% CI 1.009–1.232; p = 0.03). Conclusions: Altered miRNA 122-5p levels in HD patients may reflect hepatic and CV damage and may impart important prognostic information for improving CV risk prediction in this particular setting.

Abstract Scientific evidence points to a shared neural representation between performing and observing an action. The action observation notoriously determines a modulation of the observer’s sensorimotor system, a phenomenon called Motor Resonance (MR). Fibromyalgia (FM) patients suffer from a condition characterized by generalized musculoskeletal pain in which even simple movement can exacerbate their symptoms. Maladaptive functioning of the primary motor cortex is a common finding in patients with chronic pain. Activation of the motor cortex is known to induce an analgesic effect in patients with chronic pain. In this exploratory study, we intend to verify if the mere observation of a movement could elicit activation of the motor cortical areas in patients with FM. Therefore, the purpose of this study was to examine the presence of MR in patients affected by fibromyalgia. We adopted a behavioral paradigm known for detecting the presence of MR and a neurophysiological experiment. Participants watched videos showing gripping movements towards a graspable or an ungraspable object, respectively, and were asked to press a button the instant the agent touched the object (Time-to-contact detection session). In a different experimental session, participants were only requested to observe and pay attention to the videos (Observation-only session). During each experimental session, the participants’ cerebral hemodynamic activity was recorded using the functional Near-Infrared Spectroscopy method. The behavioral task analysis revealed the presence of MR in both FM patients and healthy controls. Moreover, neurophysiological findings suggested that the observation of movement during the Observation-only session provoked activation and modulation of the cortical motor networks of FM patients. These results could represent evidence of the possible beneficial effects of movement observation in restarting motor activation, notoriously reduced, in FM patients.

The recent advancements in the surging field of Deep Learning (DL) have revolutionized every sphere of life, and the healthcare domain is no exception. The enormous success of DL models, particularly with image data, has led to the development of image-guided Robot Assisted Surgery (RAS) systems. By and large, the number of studies concerning image-driven computer assisted surgical systems using DL has increased exponentially. Additionally, the contemporary availability of surgical datasets has also boosted the DL applications in RAS. Inspired by the latest trends and contributions in surgery, this literature survey presents a summarized analysis of recent innovations of DL in image-guided RAS systems. After a thorough review, a sum of 184 articles are selected and grouped into four categories, based on the literature and the relevancy of the task in the articles, comprising 1) Surgical Tools, 2) Surgical Processes, 3) Surgical Surveillance, and 4) Surgical Performance. The survey also discusses publicly available surgical datasets and highlights the basics of the DL models. Furthermore, the legal, ethical, and technological challenges together with the intuitive predictions and recommendations related to the autonomous RAS systems are also presented. The study reveals that Convolutional Neural Network (CNN) is most widely adopted architecture, whereas, the JIGSAWS is most employed dataset in RAS. The study suggests fusing kinematic data along with image data, which produces better accuracy and precision, particularly in gesture and trajectory segmentation tasks. Additionally, CNN and long short term memory networks have shown remarkable performance, however, authors recommend employing these gigantic architectures only when simpler models have failed to produce satisfactory results. The simpler models, despite their limitations, are time and cost effective and yield considerable outcomes even on the smaller datasets.

Abstract Background Computer-aided diagnosis (CAD) systems based on medical images could support physicians in the decision-making process. During the last decades, researchers have proposed CAD systems in several medical domains achieving promising results. CAD systems play an important role in digital pathology supporting pathologists in analyzing biopsy slides by means of standardized and objective workflows. In the proposed work, we designed and tested a novel CAD system module based on image processing techniques and machine learning, whose objective was to classify the condition affecting renal corpuscles (glomeruli) between sclerotic and non-sclerotic. Such discrimination is useful for the biopsy slides evaluation performed by pathologists. Results We collected 26 digital slides taken from the kidneys of 19 donors with Periodic Acid-Schiff staining. Expert pathologists have conducted the slides preparation, digital acquisition and glomeruli annotations. Before setting the classifiers, we evaluated several feature extraction techniques from the annotated regions. Then, a feature reduction procedure followed by a shallow artificial neural network allowed discriminating between the glomeruli classes. We evaluated the workflow considering an independent dataset (i.e., processing images not used in the training procedure). Ten independent runs of the training algorithm, and evaluation, allowed achieving MCC and Accuracy of 0.95 (± 0.01) and 0.99 (standard deviation

The outdoor sculpture of the first Portuguese king, D. Afonso Henriques (c. 1109–1185 AD), placed in Guimarães (North Portugal), is one of the most emblematic national sculptures. Created in 1887 by António Soares dos Reis, it possesses a remarkable symbolic value in the presumed birthplace of the king. In addition to the artistic and heritage importance of the monument, it is one of the few sculptures cast by a Portuguese industrial foundry in the 19th century. This study obtained data on the sculpture’s elemental composition and corrosion products, gathering important historical and technical information. For this purpose, a multi-analytical approach consisting of X-ray fluorescence (XRF), X-ray diffraction (XRD), optical microscopy (OM) and scanning electron microscopy (SEM-EDS) was carried out to characterise the bulk metal and corrosion layers. The data revealed a ternary alloy of Cu, Sn and Zn, with Pb, Fe, As, Bi and Mn as minor elements. The alloy matches that of other sculptures cast in that period. In terms of corrosion, it is characterised by the presence of oxides. These results represent the first step for applying an appropriate conservation strategy for bronze sculptures with similar characteristics.

IntroductionGerm cell tumors (GCTs) are the most common type of cancer in young men. These tumors usually originate from the testis, but they can occasionally develop from extragonadal sites probably due to primordial germ cells (PGCs) migration errors. Cisplatin-based chemotherapy is usually effective for male GCTs, but the risk of toxicity is high and new therapeutic strategies are needed. Although Metformin (Met) has been widely studied as a potential cancer treatment over the past decades, there is limited evidence to support its use in treating male GCTs. Additionally, the mechanism by which it acts on tumor cells is still not entirely understood.MethodsSEM-1 cells, a newly established human cell line of extragonadal origin, were treated with Met. Cell viability was studied by MTT assay, while cell migration and invasion were studied by the wound healing assay and the transwell assay, respectively. The effect of Met on 3D spheroid formation was determined by seeding SEM-1 cells in appropriate cell suspension culture conditions, and cell cycle was characterized by flow cytometry. Factors involved in PGCs migration and GCT invasion, such as IGFBP1, IGF1R, MMP-11 and c-Kit, together with cyclin D1 (a key regulator of cell cycle progression), and the upstream factor, HMGA1, were determined by immunoblots.ResultsTreatment of SEM-1 cells with Met resulted in a potent and dose-dependent reduction of cell proliferation, as evidenced by decreased nuclear abundance of cyclin D1 and cell cycle arrest in G1 phase. Also, Met prevented the formation of 3D spheroids, and blocked cell migration and invasion by reducing the expression of IGFBP1, IGF1R and MMP-11. Both, IGFBP1 and MMP-11 are under control of HMGA1, a chromatin-associated protein that is involved in the regulation of important oncogenic, metabolic and embryological processes. Intriguingly, an early reduction in the nuclear abundance of HMGA1 occurred in SEM-1 cells treated with Met.ConclusionsOur results document the antiproliferative and antimigratory effects of Met in SEM-1 cells, providing new insights into the potential treatments for male GCTs. The anticancer properties of Met in SEM-1 cells are likely related to its ability to interfere with HMGA1 and downstream targets, including cyclin D1, the IGFs system, and MMP-11.

In recent years, there has been a dramatic increase in the number of pregnancies complicated by gestational diabetes mellitus (GDM). GDM occurs when maternal insulin resistance develops and/or progresses during gestation, and it is not compensated by a rise in maternal insulin secretion. If not properly managed, this condition can cause serious short-term and long-term problems for both mother and child. Lifestyle changes are the first line of treatment for GDM, but if ineffective, insulin injections are the recommended pharmacological treatment choice. Some guidance authorities and scientific societies have proposed the use of metformin as an alternative pharmacological option for treating GDM, but there is not yet a unanimous consensus on this. Although the use of metformin appears to be safe for the mother, concerns remain about its long-term metabolic effects on the child that is exposed in utero to the drug, given that metformin, contrary to insulin, crosses the placenta. This review article describes the existing lines of evidence about the use of metformin in pregnancies complicated by GDM, in order to clarify its potential benefits and limits, and to help clinicians make decisions about who could benefit most from this drug treatment.

Background and aim—Alterations in circulating microRNA (miRNA) expression patterns are thought to be involved in the early stages of prediabetes, as well as in the progression to overt type 2 diabetes mellitus (T2D) and its vascular complications. However, most research findings are conflicting, in part due to differences in miRNA extraction and normalization methods, and in part due to differences in the study populations and their selection. This cross-sectional study seeks to find new potentially useful biomarkers to predict and/or diagnose T2D by investigating the differential expression patterns of circulating miRNAs in the serum of patients with impaired fasting glucose (IFG) and new-onset T2D, with respect to euglycemic controls, using a high-throughput 384-well array and real-time PCR. Methods—Thirty subjects, aged 45–65 years, classified into three matched groups (of 10 participants each) according to their glycometabolic status, namely (1) healthy euglycemic controls, (2) patients with IFG and (3) patients with new-onset, uncomplicated T2D (

Miniaturized bronze flasks represent a small portion of a wide metallurgical production that flourished in Sardinia (Italy) between the Final Bronze Age (FBA) and the Early Iron Age (EIA). They replicate a well-known and symbolic type of object, the pilgrims’ flask, common in all Europe and Mediterranean basin, and have but few archaeological parallels. For these reasons, their characterization can be considered important from an archaeological perspective. Three flasks, preserved at the Antiquarium Arborense museum (Oristano), were analyzed by X-Ray Fluorescence Spectroscopy (XRF) and Raman spectroscopy, integrated by multispectral images. The samples, coming from illegal excavations, posed two problems: establishing their authenticity and investigating the alloy composition of such particular objects. All specimens presented a widespread degradation in the outer surface: XRF and Raman spectroscopy indicated the presence of copper oxides, calcium and copper carbonates deposits. The abscence of Zn, a clear marker of forgeries, was not detected by XRF. In two of the flasks, an unusual Sn content above 20%, was detected. For FBA and EIA, especially regarding southern Europe, Sn was extremely rare, and was possibly used with caution. Further results are presented herein.

The high artistic and cultural relevance of particular objects, in this case from the Nuragic civilization, have stimulated the growth of a forgery industry, replicating small bronze boats (navicelle), statues (bronzetti), and other objects. It is often the case where the forgeries are of such quality that it becomes difficult to distinguish them from authentic artifacts without a proper chemical analysis. In this research, a Monte Carlo simulation algorithm for X-ray interactions with matter is used to obtain the chemical composition from the bulk of each object from a set of five. The method employed has the advantage of being completely nondestructive and relatively fast. The objects’ chemical composition and morphology were compared with the data available from authentic artifacts so their authenticity could be inferred. Four of the five objects are likely to be authentic, where two of them could be associated with a Sardinian origin.

We report a case of a 50-year-old male admitted to the Endocrinology Unit because of persistent headaches, nausea, feeling tired, sudden weight loss, cold intolerance, decreased appetite, and lack of sex interest. Diagnostic workup showed a 6-millimeter pituitary tumor without signs of compression, and a condition of progressive panhypopituitarism. After 12 months of hormone replacement therapy, the patient was hospitalized because of sudden weight gain, periorbital-peripheral edema, severe dyslipidemia, hypertension, and proteinuria. Corticosteroid therapy was shifted from oral to continuous intravenous infusion, and once the diagnosis of “immune complex-mediated glomerulonephritis with mesangial deposits suggestive for membranoproliferative glomerulonephritis type IIIIgG4-positive” was made, the immunosuppressant mycophenolate (1500 mg/day) was started. After a 6-month follow-up, the complete resolution of renal symptoms was accompanied by the disappearance of a pituitary lesion and the patient was back to prior hormone replacement therapy. Autoimmune hypophysitis (AH) is a rare autoimmune inflammatory disease of the pituitary gland that can impair hormone secretion and function. IgG4-hypophysitis is rare and is usually associated with other IgG4-related diseases. Herein, we describe a rare case of AH associated with late renal disease, and without any other organ involvement.

The complex pathobiology of lung cancer, and its spread worldwide, has prompted research studies that combine radiomic and genomic approaches. Indeed, the early identification of genetic alterations and driver mutations affecting the tumor is fundamental for correctly formulating the prognosis and therapeutic response. In this work, we propose a radiogenomic workflow to detect the presence of KRAS and EGFR mutations using radiomic features extracted from computed tomography images of patients affected by lung adenocarcinoma. To this aim, we investigated several feature selection algorithms to identify the most significant and uncorrelated sets of radiomic features and different classification models to reveal the mutational status. Then, we employed the SHAP (SHapley Additive exPlanations) technique to increase the understanding of the contribution given by specific radiomic features to the identification of the investigated mutations. Two cohorts of patients with lung adenocarcinoma were used for the study. The first one, obtained from the Cancer Imaging Archive (TCIA), consisted of 60 cases (25% EGFR, 23% KRAS); the second one, provided by the Azienda Ospedaliero-Universitaria ’Ospedali Riuniti’ of Foggia, was composed of 55 cases (16% EGFR, 28% KRAS). The best-performing models proposed in our study achieved an AUC of 0.69 and 0.82 on the validation set for predicting the mutational status of EGFR and KRAS, respectively. The Multi-layer Perceptron model emerged as the top-performing model for both oncogenes, in some cases outperforming the state of the art. This study showed that radiomic features can be associated with EGFR and KRAS mutational status in patients with lung adenocarcinoma.

The study of visuomotor adaptation (VMA) capabilities has been encompassed in various experimental protocols aimed at investigating human motor control strategies and/or cognitive functions. VMA-oriented frameworks can have clinical applications, primarily in the investigation and assessment of neuromotor impairments caused by conditions such as Parkinson’s disease or post-stroke, which affect the lives of tens of thousands of people worldwide. Therefore, they can enhance the understanding of the specific mechanisms of such neuromotor disorders, thus being a potential biomarker for recovery, with the aim of being integrated with conventional rehabilitative programs. Virtual Reality (VR) can be entailed in a framework targeting VMA since it allows the development of visual perturbations in a more customizable and realistic way. Moreover, as has been demonstrated in previous works, a serious game (SG) can further increase engagement thanks to the use of full-body embodied avatars. Most studies implementing VMA frameworks have focused on upper limb tasks and have utilized a cursor as visual feedback for the user. Hence, there is a paucity in the literature about VMA-oriented frameworks targeting locomotion tasks. In this article, the authors present the design, development, and testing of an SG-based framework that addresses VMA in a locomotion activity by controlling a full-body moving avatar in a custom VR environment. This workflow includes a set of metrics to quantitatively assess the participants’ performance. Thirteen healthy children were recruited to evaluate the framework. Several quantitative comparisons and analyses were run to validate the different types of introduced visuomotor perturbations and to evaluate the ability of the proposed metrics to describe the difficulty caused by such perturbations. During the experimental sessions, it emerged that the system is safe, easy to use, and practical in a clinical setting. Despite the limited sample size, which represents the main limitation of the study and can be compensated for with future recruitment, the authors claim the potential of this framework as a useful instrument for quantitatively assessing either motor or cognitive impairments. The proposed feature-based approach gives several objective parameters as additional biomarkers that can integrate the conventional clinical scores. Future studies might investigate the relation between the proposed biomarkers and the clinical scores for specific disorders such as Parkinson’s disease and cerebral palsy.

The autosomal dominant familial form of neurohypophyseal diabetes insipidus (adFNDI) is a rare inherited endocrine disorder characterized by hypotonic polyuria, severe thirst and polydipsia, which results from a deficient neurosecretion of the antidiuretic hormone, also known as arginine vasopressin (AVP). To date, adFNDI has been linked to more than 70 different heterozygous point mutations of the 2.5 kb AVP gene, encoding the composite precursor protein of AVP. A minority of disease-causing mutations, such as the common c.55G>A variant, are predicted to affect amino acid residues close to the signal peptide (SP) cleavage site, and result in abnormal post-translational processing and intracellular trafficking of AVP precursors exerting neurotoxic activity on vasopressinergic magnocellular neurons. Generally, SP variants cause a gradual decline in the neurohypophyseal secretion of AVP in small children, although a wide variability in clinical onset and severity of manifestations has been reported. For the first time, we describe a kindred from Calabria (Southern Italy) with adFNDI and document a partial clinical phenotype in one female young adult member of the family. Methods: A young adult woman was subjected to clinical, neuroradiological and genetic assessments for a mild, adolescent-onset, polyuric state at our Endocrinology Unit. Her family medical history revealed an early-onset (A transition, predicting a p.Ala19Thr replacement in the C-terminal region of SP, was the cause of adFNDI in the investigated kindred. Conclusions: The identification of the genetic cause of aFNDI in this Calabrian kindred provides further information and confirms the wide variability of disease onset and severity of manifestations related to SP variants of the AVP gene, supporting the need for genetic testing in all patients with familial occurrence of polyuria, regardless of their clinical and radiological phenotype. Even though sexual differences in the antidiuretic responses are documented, it is unclear whether female gender would attenuate clinical disease progression in the presence of a pathogenic c.55G>A mutation.

Ionizing radiation (IR) can induce some associated pathological conditions due to numerous cell damages. The influence of sex is scarcely known, and even less known is whether the effect of antioxidants is sex-dependent. Given the increased use of IR, we investigated whether male human umbilical vein endothelial cells (MHUVECs) and female human umbilical vein endothelial cells (FHUVECs) respond differently to IR exposure and whether the antioxidants 10 mM taurine (TAU) and 5 mM N-acetylcysteine (NAC) can prevent IR-induced damage in a sex-dependent way. In untreated cells, sex differences were observed only during autophagy, which was higher in FHUVECs. In non-irradiated cells, preincubation with TAU and NAC did not modify viability, lactate dehydrogenase (LDH) release, migration, or autophagy, whereas only NAC increased malondialdehyde (MDA) levels in FHUVECs. X-ray irradiation increased LDH release and reduced viability and migration in a sex-independent manner. TAU and NAC did not affect viability while reduced LDH release in irradiated cells: they have the same protective effect in FHUVECs, while, TAU was more protective than NAC in male cells.. Moreover, TAU and NAC significantly promoted the closure of wounds in both sexes in irradiated cells, but NAC was more effective at doing this in FHUVECs. In irradiated cells, TAU did not change autophagy, while NAC attenuated the differences between the sexes. Finally, NAC significantly decreased MDA in MHUVECs and increased MDA in FHUVECs. In conclusion, FHUVECs appear to be more susceptible to IR damage, and the effects of the two antioxidants present some sex differences, suggesting the need to study the influence of sex in radiation mitigators.

In the last 10 years, the demand for robot-based depalletization systems has constantly increased due to the growth of sectors such as logistics, storage, and supply chains. Since the scenarios are becoming more and more unstructured, characterized by unknown pallet layouts and stock-keeping unit shapes, the classical depalletization systems based on the knowledge of predefined positions within the pallet frame are going to be substituted by innovative and robust solutions based on 2D/3D vision and Deep Learning (DL) methods. In particular, the Convolutional Neural Networks (CNNs) are deep networks that have proven to be effective in processing 2D/3D images, for example in the automatic object detection task, and robust to the possible variability among the data. However, deep neural networks need a big amount of data to be trained. In this context, whenever deep networks are involved in object detection for supporting depalletization systems, the dataset collection represents one of the main bottlenecks during the commissioning phase. The present work aims at comparing different training strategies to customize an object detection model aiming at minimizing the number of images required for model fitting, while ensuring reliable and robust performances. Different approaches based on a CNN for object detection are proposed, evaluated, and compared in terms of the F1-score. The study was conducted considering different starting conditions in terms of the neural network’s weights, the datasets, and the training set sizes. The proposed approaches were evaluated on the detection of different kinds of paper boxes placed on an industrial pallet. The outcome of the work validates that the best strategy is based on fine-tuning of a CNN-based model already trained on the detection of paper boxes, with a median F1-score greater than 85.0%.

Abstract Background Assessment and rating of Parkinson’s Disease (PD) are commonly based on the medical observation of several clinical manifestations, including the analysis of motor activities. In particular, medical specialists refer to the MDS-UPDRS (Movement Disorder Society – sponsored revision of Unified Parkinson’s Disease Rating Scale) that is the most widely used clinical scale for PD rating. However, clinical scales rely on the observation of some subtle motor phenomena that are either difficult to capture with human eyes or could be misclassified. This limitation motivated several researchers to develop intelligent systems based on machine learning algorithms able to automatically recognize the PD. Nevertheless, most of the previous studies investigated the classification between healthy subjects and PD patients without considering the automatic rating of different levels of severity. Methods In this context, we implemented a simple and low-cost clinical tool that can extract postural and kinematic features with the Microsoft Kinect v2 sensor in order to classify and rate PD. Thirty participants were enrolled for the purpose of the present study: sixteen PD patients rated according to MDS-UPDRS and fourteen healthy paired subjects. In order to investigate the motor abilities of the upper and lower body, we acquired and analyzed three main motor tasks: (1) gait, (2) finger tapping, and (3) foot tapping. After preliminary feature selection, different classifiers based on Support Vector Machine (SVM) and Artificial Neural Networks (ANN) were trained and evaluated for the best solution. Results Concerning the gait analysis, results showed that the ANN classifier performed the best by reaching 89.4% of accuracy with only nine features in diagnosis PD and 95.0% of accuracy with only six features in rating PD severity. Regarding the finger and foot tapping analysis, results showed that an SVM using the extracted features was able to classify healthy subjects versus PD patients with great performances by reaching 87.1% of accuracy. The results of the classification between mild and moderate PD patients indicated that the foot tapping features were the most representative ones to discriminate (81.0% of accuracy). Conclusions The results of this study have shown how a low-cost vision-based system can automatically detect subtle phenomena featuring the PD. Our findings suggest that the proposed tool can support medical specialists in the assessment and rating of PD patients in a real clinical scenario.

Abstract Background The automatic segmentation of kidneys in medical images is not a trivial task when the subjects undergoing the medical examination are affected by Autosomal Dominant Polycystic Kidney Disease (ADPKD). Several works dealing with the segmentation of Computed Tomography images from pathological subjects were proposed, showing high invasiveness of the examination or requiring interaction by the user for performing the segmentation of the images. In this work, we propose a fully-automated approach for the segmentation of Magnetic Resonance images, both reducing the invasiveness of the acquisition device and not requiring any interaction by the users for the segmentation of the images. Methods Two different approaches are proposed based on Deep Learning architectures using Convolutional Neural Networks (CNN) for the semantic segmentation of images, without needing to extract any hand-crafted features. In details, the first approach performs the automatic segmentation of images without any procedure for pre-processing the input. Conversely, the second approach performs a two-steps classification strategy: a first CNN automatically detects Regions Of Interest (ROIs); a subsequent classifier performs the semantic segmentation on the ROIs previously extracted. Results Results show that even though the detection of ROIs shows an overall high number of false positives, the subsequent semantic segmentation on the extracted ROIs allows achieving high performance in terms of mean Accuracy. However, the segmentation of the entire images input to the network remains the most accurate and reliable approach showing better performance than the previous approach. Conclusion The obtained results show that both the investigated approaches are reliable for the semantic segmentation of polycystic kidneys since both the strategies reach an Accuracy higher than 85%. Also, both the investigated methodologies show performances comparable and consistent with other approaches found in literature working on images from different sources, reducing both the invasiveness of the analyses and the interaction needed by the users for performing the segmentation task.

Abstract Background Handwriting represents one of the major symptom in Parkinson’s Disease (PD) patients. The computer-aided analysis of the handwriting allows for the identification of promising patterns that might be useful in PD detection and rating. In this study, we propose an innovative set of features extracted by geometrical, dynamical and muscle activation signals acquired during handwriting tasks, and evaluate the contribution of such features in detecting and rating PD by means of artificial neural networks. Methods Eleven healthy subjects and twenty-one PD patients were enrolled in this study. Each involved subject was asked to write three different patterns on a graphic tablet while wearing the Myo Armband used to collect the muscle activation signals of the main forearm muscles. We have then extracted several features related to the written pattern, the movement of the pen and the pressure exerted with the pen and the muscle activations. The computed features have been used to classify healthy subjects versus PD patients and to discriminate mild PD patients from moderate PD patients by using an artificial neural network (ANN). Results After the training and evaluation of different ANN topologies, the obtained results showed that the proposed features have high relevance in PD detection and rating. In particular, we found that our approach both detect and rate (mild and moderate PD) with a classification accuracy higher than 90%. Conclusions In this paper we have investigated the representativeness of a set of proposed features related to handwriting tasks in PD detection and rating. In particular, we used an ANN to classify healthy subjects and PD patients (PD detection), and to classify mild and moderate PD patients (PD rating). The implemented and tested methods showed promising results proven by the high level of accuracy, sensitivity and specificity. Such results suggest the usability of the proposed setup in clinical settings to support the medical decision about Parkinson’s Disease.

Nuclei identification is a fundamental task in many areas of biomedical image analysis related to computational pathology applications. Nowadays, deep learning is the primary approach by which to segment the nuclei, but accuracy is closely linked to the amount of histological ground truth data for training. In addition, it is known that most of the hematoxylin and eosin (H&E)-stained microscopy nuclei images contain complex and irregular visual characteristics. Moreover, conventional semantic segmentation architectures grounded on convolutional neural networks (CNNs) are unable to recognize distinct overlapping and clustered nuclei. To overcome these problems, we present an innovative method based on gradient-weighted class activation mapping (Grad-CAM) saliency maps for image segmentation. The proposed solution is comprised of two steps. The first is the semantic segmentation obtained by the use of a CNN; then, the detection step is based on the calculation of local maxima of the Grad-CAM analysis evaluated on the nucleus class, allowing us to determine the positions of the nuclei centroids. This approach, which we denote as NDG-CAM, has performance in line with state-of-the-art methods, especially in isolating the different nuclei instances, and can be generalized for different organs and tissues. Experimental results demonstrated a precision of 0.833, recall of 0.815 and a Dice coefficient of 0.824 on the publicly available validation set. When used in combined mode with instance segmentation architectures such as Mask R-CNN, the method manages to surpass state-of-the-art approaches, with precision of 0.838, recall of 0.934 and a Dice coefficient of 0.884. Furthermore, performance on the external, locally collected validation set, with a Dice coefficient of 0.914 for the combined model, shows the generalization capability of the implemented pipeline, which has the ability to detect nuclei not only related to tumor or normal epithelium but also to other cytotypes.

Background and Objectives: Thyroid dysfunction is associated with non-alcoholic fatty liver disease, but its role in the progression of liver damage in obese patients remains unclear. In addition, several case reports have suggested the existence of a levothyroxine-induced liver injury, which has been poorly investigated. Our aim was to verify whether a difference in the prevalence of liver fibrosis exists in a population of obese individuals taking Levothyroxine. Materials and Methods: We conducted a cross-sectional study on a population of 137 obese individuals, of which 49 were on replacement therapy with Levothyroxine. We excluded those who had hypertriglyceridemia and diabetes mellitus. All participants underwent a liver stiffness assessment by transient elastography as well as biochemical measurements. In subjects with liver fibrosis, other cause of liver fibrosis were ruled out. Results: Participants taking Levothyroxine had a higher prevalence of liver fibrosis than those not taking Levothyroxine (30.6% vs. 2.3%; p < 0.001), and these results were obtained after we made an adjustment for age (Exp(B) = 18.9; 95% CI = 4.1–87.4; p < 0.001). The liver stiffness value differed significantly between groups (6.0 ± 3.6 and 5.1 ± 1.2, p = 0.033). Of those subjects taking Levothyroxine, there were no significant differences in the dose of medication (1.21 ± 0.36 vs. 1.07 ± 0.42; p = 0.240) and treatment duration (13.7 ± 7.43 vs. 11.13 ± 6.23; p = 0.380) between those with and without liver fibrosis. Conclusions: We found, for the first time, a greater prevalence of liver fibrosis in obese individuals taking Levothyroxine than in those not taking this medication. This finding needs to be confirmed by longitudinal population studies as well as by cellular studies.

In prostate cancer, fusion biopsy, which couples magnetic resonance imaging (MRI) with transrectal ultrasound (TRUS), poses the basis for targeted biopsy by allowing the comparison of information coming from both imaging modalities at the same time. Compared with the standard clinical procedure, it provides a less invasive option for the patients and increases the likelihood of sampling cancerous tissue regions for the subsequent pathology analyses. As a prerequisite to image fusion, segmentation must be achieved from both MRI and TRUS domains. The automatic contour delineation of the prostate gland from TRUS images is a challenging task due to several factors including unclear boundaries, speckle noise, and the variety of prostate anatomical shapes. Automatic methodologies, such as those based on deep learning, require a huge quantity of training data to achieve satisfactory results. In this paper, the authors propose a novel optimization formulation to find the best superellipse, a deformable model that can accurately represent the prostate shape. The advantage of the proposed approach is that it does not require extensive annotations, and can be used independently of the specific transducer employed during prostate biopsies. Moreover, in order to show the clinical applicability of the method, this study also presents a module for the automatic segmentation of the prostate gland from MRI, exploiting the nnU-Net framework. Lastly, segmented contours from both imaging domains are fused with a customized registration algorithm in order to create a tool that can help the physician to perform a targeted prostate biopsy by interacting with the graphical user interface.

Computer-aided diagnosis (CAD) systems can help radiologists in numerous medical tasks including classification and staging of the various diseases. The 3D tomosynthesis imaging technique adds value to the CAD systems in diagnosis and classification of the breast lesions. Several convolutional neural network (CNN) architectures have been proposed to classify the lesion shapes to the respective classes using a similar imaging method. However, not only is the black box nature of these CNN models questionable in the healthcare domain, but so is the morphological-based cancer classification, concerning the clinicians. As a result, this study proposes both a mathematically and visually explainable deep-learning-driven multiclass shape-based classification framework for the tomosynthesis breast lesion images. In this study, authors exploit eight pretrained CNN architectures for the classification task on the previously extracted regions of interests images containing the lesions. Additionally, the study also unleashes the black box nature of the deep learning models using two well-known perceptive explainable artificial intelligence (XAI) algorithms including Grad-CAM and LIME. Moreover, two mathematical-structure-based interpretability techniques, i.e., t-SNE and UMAP, are employed to investigate the pretrained models’ behavior towards multiclass feature clustering. The experimental results of the classification task validate the applicability of the proposed framework by yielding the mean area under the curve of 98.2%. The explanability study validates the applicability of all employed methods, mainly emphasizing the pros and cons of both Grad-CAM and LIME methods that can provide useful insights towards explainable CAD systems.

Vaginal infections affect millions of women annually worldwide. Therapeutic options are limited, moreover drug-resistance increases the need to find novel antimicrobials for health promotion. Recently phytochemicals were re-discovered for medical treatment. Myrtle (Myrtus communis L.) plant extracts showed in vitro antioxidant, antiseptic and anti-inflammatory properties thanks to their bioactive compounds. The aim of the present study was to create novel nanodevices to deliver three natural extracts from leaves, seeds and fruit of myrtle, in vaginal milieu. We explored their effect on human cells (HeLa, Human Foreskin Fibroblast-1 line, and stem cells isolated from skin), resident microflora (Lactobacillus acidophilus) and on several vaginal pathogens (Trichomonas vaginalis, Escherichia coli, Staphylococcus aureus, Candida albicans, Candida kefyr, Candida glabrata, Candida parapsilosis, Candida krusei). Polycaprolactone-Gelatin nanofibers encapsulated with leaves extract and soaked with seed extracts exhibited a different capability in regard to counteracting microbial proliferation. Moreover, these nanodevices do not affect human cells and resident microflora viability. Results reveal that some of the tested nanofibers are interesting candidates for future vaginal infection treatments.

Lung cancer is one of the deadliest diseases worldwide. Computed Tomography (CT) images are a powerful tool for investigating the structure and texture of lung nodules. For a long time, trained radiologists have performed the grading and staging of cancer severity by relying on radiographic images. Recently, radiomics has been changing the traditional workflow for lung cancer staging by providing the technical and methodological means to analytically quantify lesions so that more accurate predictions could be performed while reducing the time required from each specialist to perform such tasks. In this work, we implemented a pipeline for identifying a radiomic signature composed of a reduced number of features to discriminate between adenocarcinomas and other cancer types. In addition, we also investigated the reproducibility of this radiomic study analysing the performances of the classification models on external validation data. In detail, we first considered two publicly available datasets, namely D1 and D2, composed of n = 262 and n = 89 samples, respectively. Ten significant features, according to univariate AUC evaluated on D1, were retained. Mann–Whitney U tests recognised three of these features to have a statistically different distribution, with a p-value < 0.05. Then, we collected n = 51 CT images from patients with lung nodules at the Azienda Ospedaliero—Universitaria “Policlinico Riuniti” in Foggia. Resident radiologists manually annotated the lung lesions in images to allow the subsequent analysis of the malignancy regions. We designed a pipeline for feature extraction from the Volumes of Interest in order to generate a third dataset, i.e., D3. Several experiments have been performed showing that the selected radiomic signature not only allowed the discrimination of lung adenocarcinoma from other cancer types independently from the input dataset used for training the models, but also allowed reaching good classification performances also on external validation data; in fact, the radiomic signature computed on D1 and evaluated on the local cohort allowed reaching an AUC of 0.70 (p<0.001) for the task of predicting the histological subtype.

The non-destructive testing methods offer great benefit in detecting and classifying the weld defects. Among these, infrared (IR) thermography stands out in the inspection, characterization, and analysis of the defects from the camera image sequences, particularly with the recent advent of deep learning. However, in IR, the defect classification becomes a cumbersome task because of the exposure to the inconsistent and unbalanced heat source, which requires additional supervision. In light of this, authors present a fully automated system capable of detecting defective welds according to the electrical resistance properties in the inline mode. The welding process is captured by an IR camera that generates a video sequence. A set of features extracted by such video feeds supervised machine learning and deep learning algorithms in order to build an industrial diagnostic framework for weld defect detection. The experimental study validates the aptitude of a customized convolutional neural network architecture to classify the malfunctioning weld joints with mean accuracy of 99% and median f1 score of 73% across five-fold cross validation on our locally acquired real world dataset. The outcome encourages the integration of thermographic-based quality control frameworks in all applications where fast and accurate recognition and safety assurance are crucial industrial requirements across the production line.

Objective: Recently, the role of circulating miRNAs as non-invasive biomarkers for the identification and monitoring of diabetes microvascular complications has emerged. Herein, we aimed to: identify circulating miRNAs differentially expressed in patients with and without diabetic retinopathy (DR); examine their predictive value; and understand their pathogenic impact.Methods: Pooled serum samples from randomly selected matched patients with type 2 diabetes, either with or without DR, were used for initial serum miRNA profiling. Validation of the most relevant miRNAs was thereafter conducted by RT-qPCR in an extended sample of patients with DR and matched controls.Results: Following miRNA profiling, 43 miRNAs were significantly up- or down-regulated in patients with DR compared with controls. After individual validation, 5 miRNAs were found significantly overexpressed in patients with DR. One of them, miR-1281, was the most up-regulated and appeared to be specifically related to DR. Furthermore, secreted levels of miR-1281 were increased in high glucose-cultured retinal cells, and there was evidence of a potential link between glucose-induced miR-1281 up-regulation and DR.Conclusion: Our findings suggest miR-1281 as a circulating biomarker of DR. Also, they highlight the pathogenic significance of miR-1281, providing insights for a new potential target in treating DR.

BACKGROUND:Experimental and clinical studies suggested an analgesic effect on chronic pain by motor cortex activation. The present study explored the complex mechanisms of interaction between motor and pain during performing the slow and fast finger tapping task alone and in concomitant with nociceptive laser stimulation. METHOD:The participants were 38 patients with fibromyalgia (FM) and 21 healthy subjects. We used a simultaneous multimodal method of laser-evoked potentials and functional near-infrared spectroscopy to investigate metabolic and electrical changes during the finger tapping task and concomitant noxious laser stimulation. Functional near-infrared spectroscopy is a portable and optical method to detect cortical metabolic changes. Laser-evoked potentials are a suitable tool to study the nociceptive pathways function. RESULTS:We found a reduced tone of cortical motor areas in patients with FM compared to controls, especially during the fast finger tapping task. FM patients presented a slow motor performance in all the experimental conditions, requesting rapid movements. The amplitude of laser evoked potentials was different between patients and controls, in each experimental condition, as patients showed smaller evoked responses compared to controls. Concurrent phasic pain stimulation had a low effect on motor cortex metabolism in both groups nor motor activity changed laser evoked responses in a relevant way. There were no correlations between Functional Near-Infrared Spectroscopy (FNIRS) and clinical features in FM patients. CONCLUSION:Our findings indicated that a low tone of motor cortex activation could be an intrinsic feature in FM and generate a scarce modulation on pain condition. A simple and repetitive movement such as that of the finger tapping task seems inefficacious in modulating cortical responses to pain both in patients and controls. The complex mechanisms of interaction between networks involved in pain control and motor function require further studies for the important role they play in structuring rehabilitation strategies.

Background. Screening strategies for gestational diabetes mellitus (GDM) earlier than 24-28 weeks of gestation should be considered to prevent adverse pregnancy outcomes. Nonetheless, there is uncertainty about which women would benefit most from early screening and which screening strategies should be offered to women with GDM. The Italian National Healthcare Service (NHS) recommendations on selective screening for GDM at 16-18 weeks of gestation are effective in preventing fetal macrosomia in high-risk (HR) women, but the appropriateness of timing and effectiveness of these recommendations in medium- (MR) and low-risk (LR) women are still controversial. Patients and Methods. We retrospectively enrolled 769 consecutive singleton pregnant women who underwent both anomaly scan at 19-21 weeks of gestation and screening for GDM at 16-18 and/or 24-28 weeks of gestation, in agreement with the NHS recommendations and risk stratification criteria. Comparison of maternal characteristics, fetal biometric parameters at anomaly scan (head circumference (HC), biparietal diameter (BPD), abdominal circumference (AC), femur length (FL), estimated fetal weight (EFW)), and neonatal birth weight (BW) percentile among risk groups was examined. Results. 219 (28.5%) women were diagnosed with GDM, while 550 (71.5%) were normal glucose-tolerant women. Out of 164 HR women, only 62 (37.8%) underwent the recommended early screening for GDM at 16-18 weeks of gestation. AC and EFW percentiles, as well as neonates’ BW percentiles, were significantly higher in HR women diagnosed with GDM at 24-28 weeks of gestation with respect to normal glucose-tolerant women, as well as MR and LR women who tested positive for GDM. Comparative analysis between MR and LR women with GDM and women with normal glucose tolerance revealed significant differences in both AC and EFW percentiles (P

This is the first study on the bony labyrinth of Cynotherium sardous, an intriguing extinct canid that inhabited Sardinia in the late Middle and Late Pleistocene. The morphological features of the cochlea indicate that C. sardous had a lower number of cochlear turns (2.25) than all extant canids. This feature, as well as the reduced length of the spiral canal, the cochlear curvature rate, and the narrow basal membrane, indicates that C. sardous had poor hearing abilities limited to high-frequency sounds with a low limit of 250 Hz and poor echolocalization skills. From the data available, it is not possible to infer whether C. sardous was unable to echolocalize its prey and relied on other senses (e.g., smell and sight) to locate them or whether the acoustic range of C. sardous was specialized for identifying the sounds produced by its most common prey to transmit signals for predator warnings or group communication. All things considered, the results obtained confirm the utility of cochlea morphological studies in reconstructing the hearing abilities of this species and in providing some suggestions about its ethology, but they fall short of providing any new sound evidence regarding the ecological role of C. sardous in the Late Pleistocene Sardinian ecosystem.

Liver segmentation is a crucial step in surgical planning from computed tomography scans. The possibility to obtain a precise delineation of the liver boundaries with the exploitation of automatic techniques can help the radiologists, reducing the annotation time and providing more objective and repeatable results. Subsequent phases typically involve liver vessels’ segmentation and liver segments’ classification. It is especially important to recognize different segments, since each has its own vascularization, and so, hepatic segmentectomies can be performed during surgery, avoiding the unnecessary removal of healthy liver parenchyma. In this work, we focused on the liver segments’ classification task. We exploited a 2.5D Convolutional Neural Network (CNN), namely V-Net, trained with the multi-class focal Dice loss. The idea of focal loss was originally thought as the cross-entropy loss function, aiming at focusing on “hard” samples, avoiding the gradient being overwhelmed by a large number of falsenegatives. In this paper, we introduce two novel focal Dice formulations, one based on the concept of individual voxel’s probability and another related to the Dice formulation for sets. By applying multi-class focal Dice loss to the aforementioned task, we were able to obtain respectable results, with an average Dice coefficient among classes of 82.91%. Moreover, the knowledge of anatomic segments’ configurations allowed the application of a set of rules during the post-processing phase, slightly improving the final segmentation results, obtaining an average Dice coefficient of 83.38%. The average accuracy was close to 99%. The best model turned out to be the one with the focal Dice formulation based on sets. We conducted the Wilcoxon signed-rank test to check if these results were statistically significant, confirming their relevance.

Smart industrial workstations for the training and evaluation of workers are an innovative approach to face the problems of manufacturing quality assessment and fast training. However, such products do not implement algorithms that are able to accurately track the pose of a hand tool that might also be partially occluded by the operator’s hands. In the best case, the already proposed systems roughly track the position of the operator’s hand center assuming that a certain task has been performed if the hand center position is close enough to a specified area. The problem of the pose estimation of 3D objects, including the hand tool, is an open and debated problem. The methods that lead to high accuracies are time consuming and require a 3D model of the object to detect, which is why they cannot be adopted for a real-time training system. The rise in deep learning has stimulated the search for better-performing vision-based solutions. Nevertheless, the problem of hand tool pose estimation for assembly and training procedures appears to not have been extensively investigated. In this study, four different vision-based methods based, respectively, on ArUco markers, OpenPose, Azure Kinect Body Tracking and the YOLO network have been proposed in order to estimate the position of a specific point of interest of the tool that has to be tracked in real-time during an assembly or maintenance procedure. The proposed approaches have been tested on a real scenario with four users handling a power drill simulating three different conditions during an assembly procedure. The performance of the methods has been evaluated and compared with the HTC Vive tracking system as a benchmark. Then, the advantages and drawbacks in terms of the accuracy and invasiveness of the method have been discussed. The authors can state that OpenPose is the most robust proposal arising from the study. The authors will investigate the OpenPose performance in more depth in further studies. The framework appears to be very interesting regarding its integration into a smart workstation for quality assessment and training.

The coronavirus disease 2019 (COVID-19) pandemic has affected hundreds of millions of individuals and caused millions of deaths worldwide. Predicting the clinical course of the disease is of pivotal importance to manage patients. Several studies have found hematochemical alterations in COVID-19 patients, such as inflammatory markers. We retrospectively analyzed the anamnestic data and laboratory parameters of 303 patients diagnosed with COVID-19 who were admitted to the Polyclinic Hospital of Bari during the first phase of the COVID-19 global pandemic. After the pre-processing phase, we performed a survival analysis with Kaplan–Meier curves and Cox Regression, with the aim to discover the most unfavorable predictors. The target outcomes were mortality or admission to the intensive care unit (ICU). Different machine learning models were also compared to realize a robust classifier relying on a low number of strongly significant factors to estimate the risk of death or admission to ICU. From the survival analysis, it emerged that the most significant laboratory parameters for both outcomes was C-reactive protein min; HR=17.963 (95% CI 6.548–49.277, p < 0.001) for death, HR=1.789 (95% CI 1.000–3.200, p = 0.050) for admission to ICU. The second most important parameter was Erythrocytes max; HR=1.765 (95% CI 1.141–2.729, p < 0.05) for death, HR=1.481 (95% CI 0.895–2.452, p = 0.127) for admission to ICU. The best model for predicting the risk of death was the decision tree, which resulted in ROC-AUC of 89.66%, whereas the best model for predicting the admission to ICU was support vector machine, which had ROC-AUC of 95.07%. The hematochemical predictors identified in this study can be utilized as a strong prognostic signature to characterize the severity of the disease in COVID-19 patients.

Health care networks for Parkinson's disease (PD) already exist and have been already proposed in the literature, but most of them are not able to analyse the vast volume of data generated from medical examinations and collected and organised in a pre-defined manner. In this work, the authors propose a novel health care network based on big data analytics for PD. The main goal of the proposed architecture is to support clinicians in the objective assessment of the typical PD motor issues and alterations. The proposed health care network has the ability to retrieve a vast volume of acquired heterogeneous data from a Data warehouse and train an ensemble SVM to classify and rate the motor severity of a PD patient. Once the network is trained, it will be able to analyse the data collected during motor examinations of a PD patient and generate a diagnostic report on the basis of the previously acquired knowledge. Such a diagnostic report represents a tool both to monitor the follow up of the disease for each patient and give robust advice about the severity of the disease to clinicians.

Background. SGLT-2 (sodium-glucose cotransporter-2) inhibitors are a novel class of oral hypoglycemic agents for the management of type 2 diabetes mellitus (T2DM). Herein, we aimed to assess the long-term effectiveness and safety of SGLT-2 inhibitors in a Southern Italy population of subjects affected by T2DM. Patients and Methods. 408 diabetic patients treated with one of the three SGLT-2 inhibitors currently available in Italy (dapagliflozin, empagliflozin, and canagliflozin), either alone or in combination with other antidiabetic drugs, were retrospectively assessed at baseline, during, and after 18 months of continuous therapy. Results. Treatment with SGLT-2 inhibitors resulted in a median decrease in HbA1c of 0.9%, with a percentage of decrement of 12 in relation to the baseline value, followed by a significant reduction (P