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Bacteria of the genus Brucella are facultative intracellular parasites that cause brucellosis, a severe animal and human disease. Recently, a group of taxonomists merged the brucellae with the primarily free-living, phylogenetically related Ochrobactrum spp. in the genus Brucella. This change, founded only on global genomic analysis and the fortuitous isolation of some opportunistic Ochrobactrum spp. from medically compromised patients, has been automatically included in culture collections and databases. We argue that clinical and environmental microbiologists should not accept this nomenclature, and we advise against its use because (i) it was presented without in-depth phylogenetic analyses and did not consider alternative taxonomic solutions; (ii) it was launched without the input of experts in brucellosis or Ochrobactrum; (iii) it applies a non-consensus genus concept that disregards taxonomically relevant differences in structure, physiology, population structure, core-pangenome assemblies, genome structure, genomic traits, clinical features, treatment, prevention, diagnosis, genus description rules, and, above all, pathogenicity; and (iv) placing these two bacterial groups in the same genus creates risks for veterinarians, medical doctors, clinical laboratories, health authorities, and legislators who deal with brucellosis, a disease that is particularly relevant in low- and middle-income countries. Based on all this information, we urge microbiologists, bacterial collections, genomic databases, journals, and public health boards to keep the Brucella and Ochrobactrum genera separate to avoid further bewilderment and harm.

IntroductionMachine Learning (ML) has emerged as a promising approach in healthcare, outperforming traditional statistical techniques. However, to establish ML as a reliable tool in clinical practice, adherence to best practices in data handling, and modeling design and assessment is crucial. In this work, we summarize and strictly adhere to such practices to ensure reproducible and reliable ML. Specifically, we focus on Alzheimer's Disease (AD) detection, a challenging problem in healthcare. Additionally, we investigate the impact of modeling choices, including different data augmentation techniques and model complexity, on overall performance.MethodsWe utilize Magnetic Resonance Imaging (MRI) data from the ADNI corpus to address a binary classification problem using 3D Convolutional Neural Networks (CNNs). Data processing and modeling are specifically tailored to address data scarcity and minimize computational overhead. Within this framework, we train 15 predictive models, considering three different data augmentation strategies and five distinct 3D CNN architectures with varying convolutional layers counts. The augmentation strategies involve affine transformations, such as zoom, shift, and rotation, applied either concurrently or separately.ResultsThe combined effect of data augmentation and model complexity results in up to 10% variation in prediction accuracy. Notably, when affine transformation are applied separately, the model achieves higher accuracy, regardless the chosen architecture. Across all strategies, the model accuracy exhibits a concave behavior as the number of convolutional layers increases, peaking at an intermediate value. The best model reaches excellent performance both on the internal and additional external testing set.DiscussionsOur work underscores the critical importance of adhering to rigorous experimental practices in the field of ML applied to healthcare. The results clearly demonstrate how data augmentation and model depth—often overlooked factors– can dramatically impact final performance if not thoroughly investigated. This highlights both the necessity of exploring neglected modeling aspects and the need to comprehensively report all modeling choices to ensure reproducibility and facilitate meaningful comparisons across studies.

Inflammation plays a pivotal role in the pathogenesis of primary and post-essential thrombocythemia or post-polycythemia vera myelofibrosis (MF) in close cooperation with the underlying molecular drivers. This inflammatory state is induced by a dynamic spectrum of inflammatory cytokines, although recent evidence points to the participation of additional soluble inflammatory mediators. Damage-associated molecular patterns (DAMPs) represent endogenous signals released upon cell death or damage which trigger a potent innate immune response. We assessed the contribution of two prototypical DAMPs, HMGB1 and S100A8/A9, to MF inflammation. Circulating HMGB1 and S100A8/A9 were elevated in MF patients in parallel to the degree of systemic inflammation and levels increased progressively during advanced disease stages. Patients with elevated DAMPs had higher frequency of adverse clinical features, such as anemia, and inferior survival, suggesting their contribution to disease progression. Monocytes, which are key players in MF inflammation, were identified as a source of S100A8/A9 but not HMGB1 release, while both DAMPs correlated with cell death parameters, such as serum LDH and cell-free DNA, indicating that passive release is an additional mechanism leading to increased DAMPs. HMGB1 and S100A8/A9 promote inflammation through binding to Toll-like receptor (TLR) 4, whereas the former also binds TLR2. Monocytes from MF patients were shown to be hyperactivated at baseline, as reflected by higher CD11b and tissue factor exposure and increased expression levels of proinflammatory cytokines IL-1β and IL-6. Patient monocytes showed preserved TLR4 and TLR2 expression and were able to mount normal or even exacerbated functional responses and cytokine upregulation following stimulation of TLR4 and TLR2. Elevated levels of endogenous TLR ligands HMGB1 and S100A8/A9 coupled to the finding of preserved or hyperreactive TLR-triggered responses indicate that DAMPs may promote monocyte activation and cytokine production in MF, fueling inflammation. Plasma IL-1β and IL-6 were elevated in MF and correlated with DAMPs levels, raising the possibility that DAMPs could contribute to cytokine generation in vivo. In conclusion, this study highlights that, in cooperation with classic proinflammatory cytokines, DAMPs represent additional inflammatory mediators that may participate in the generation of MF inflammatory state, potentially providing novel biomarkers of disease progression and new therapeutic targets.

Anorexia nervosa (AN) is an eating disorder (ED) that has seen an increase in its incidence in the last thirty years. Compared to other psychosomatic disorders, ED can be responsible for many major medical complications, moreover, in addition to the various systemic impairments, patients with AN undergo morphological and physiological changes affecting the cerebral cortex. Through immunohistochemical studies on portions of postmortem human brain of people affected by AN and healthy individuals, and western blot studies on leucocytes of young patients and healthy controls, this study investigated the role in the afore-mentioned processes of altered redox state. The results showed that the brain volume reduction in AN could be due to an increase in the rate of cell death, mainly by apoptosis, in which mitochondria, main cellular organelles affected by a decreased dietary intake, and a highly compromised intracellular redox balance, may play a pivotal role

As critical transitional ecosystems, estuaries are facing the increasingly urgent threat of salt wedge intrusion, which impacts their ecological balance as well as human-dependent activities. Accurately predicting estuary salinity is essential for water resource management, ecosystem preservation, and for ensuring sustainable development along coastlines. In this study, we investigated the application of different machine learning and deep learning models to predict salinity levels within estuarine environments. Leveraging different techniques, including Random Forest, Least-Squares Boosting, Artificial Neural Network and Long Short-Term Memory networks, the aim was to enhance the predictive accuracy in order to better understand the complex interplay of factors influencing estuarine salinity dynamics. The Po River estuary (Po di Goro), which is one of the main hotspots of salt wedge intrusion, was selected as the study area. Comparative analyses of machine learning models with the state-of-the-art physics-based Estuary box model (EBM) and Hybrid-EBM models were conducted to assess model performances. The results highlighted an improvement in the machine learning performance, with a reduction in the RMSE (from 4.22 psu obtained by physics-based EBM to 2.80 psu obtained by LSBoost-Season) and an increase in the R2 score (from 0.67 obtained by physics-based EBM to 0.85 by LSBoost-Season), computed on the test set. We also explored the impact of different variables and their contributions to the predictive capabilities of the models. Overall, this study demonstrates the feasibility and effectiveness of ML-based approaches for estimating salinity levels due to salt wedge intrusion within estuaries. The insights obtained from this study could significantly support smart management strategies, not only in the Po River estuary, but also in other location.

Abstract BackgroundAssistive technologies (ATs) have the potential to promote the quality of life and independent living of older adults and, further, to relieve the burden of formal and informal caregivers and relatives. Technological developments over the last decades have led to a boost of available ATs. However, evidence on the benefits and satisfaction with ATs in real-world applications remains scarce. ObjectiveThis prospective, real-world, pilot study tested the perceived benefit and satisfaction with different ATs in the real-world environment. MethodsCommunity-dwelling adults aged ≥65 and their relatives tested a tablet computer with a simplified interface or a smartwatch with programmable emergency contacts for 8 weeks in their everyday life. Perceived benefits and satisfaction with ATs were assessed by all older adults and their relatives using different assessment tools before and after the intervention. Outcome measures included the Technology Usage Inventory, Quebec User Evaluation of Satisfaction with Assistive Technology 2.0, and Canadian Occupational Performance Measure. ResultsA total of 17 older adults (tablet computer: n=8, 47% and smartwatch: n=9, 53%) and 16 relatives (tablet computer: n=7, 44% and smartwatch: n=9, 56%) were included in the study. The number of participants that were frail (according to the Clinical Frailty Scale) and received care was higher in the smartwatch group than in the tablet computer group. Older adults of the smartwatch group reported higher technology acceptance (Technology Usage Inventory) and satisfaction (Quebec User Evaluation of Satisfaction with Assistive Technology 2.0) scores than those of the tablet computer group, although the differences were not significant (all P>intention to uset12.3Pt8Pt8PP> ConclusionsThis study highlights the remaining obstacles for the widespread and effective application of ATs in the everyday life of older adults and their relatives. While the results do not provide evidence for a positive effect regarding communication deficits, perceived benefits could be shown for the area of safety. Future research and technical developments need to consider not only the preferences, problems, and goals of older adults but also their relatives and caregivers to improve the acceptability and effectiveness of ATs.

Abstract Background Hypertension and chronic kidney disease (CKD) pose significant public health challenges, sharing intertwined pathophysiological mechanisms. Prediabetes is recognized as a precursor to diabetes and is often accompanied by cardiovascular comorbidities such as hypertension, elevating the risk of pre-frailty and frailty. Albuminuria is a hallmark of organ damage in hypertension amplifying the risk of pre-frailty, frailty, and cognitive decline in older adults. We explored the association between albuminuria and cognitive impairment in frail older adults with prediabetes and CKD, assessing cognitive levels based on estimated glomerular filtration rate (eGFR). Methods We conducted a study involving consecutive frail older patients with hypertension recruited from March 2021 to March 2023 at the ASL (local health unit of the Italian Ministry of Health) of Avellino, Italy, followed up after three months. Inclusion criteria comprised age over 65 years, prior diagnosis of hypertension without secondary causes, prediabetes, frailty status, Montreal Cognitive Assessment (MoCA) score 15 ml/min. Results 237 patients completed the study. We examined the association between albuminuria and MoCA Score, revealing a significant inverse correlation (r: 0.8846; p

Robotic surgery has recently been used for treatment of renal cell carcinoma (RCC) and neoplastic thrombus located in the renal vein or inferior vena cava (IVC). Accurate identification of the thrombus location is crucial, and three-dimensional augmented reality (3D AR) may be valuable in achieving this. We enrolled patients with nonmetastatic RCC and level 0–I venous thrombus (Mayo Clinic classification) for robot-assisted radical nephrectomy and thrombectomy with 3D AR guidance. Five patients were prospectively enrolled; three had a level 0 thrombus and two had a level I thrombus. The mean operative time was 123 ± 15 min, mean IVC clamping time was 9.4 ± 6.8 min, and mean estimated blood loss was 750 ± 150 ml. The AR system allowed precise estimation of the thrombus location in all cases. No intraoperative complications or postoperative Clavien-Dindo grade >2 complications occurred. Use of 3D AR guidance allowed correct estimation of the limits of the thrombus and guided the surgeon in selecting an appropriate surgical strategy.

Abstract Background The broad implications of caring for a family member with a chronic medical condition, such as MS, can lead caregivers to experience a high burden of care. The aim of the study was to describe profile of MS caregivers and their burden and to explore potential factors influencing this burden. Methods 200 family caregivers of a person with MS completed survey questionnaires across a cross-sectional study. Many information were collected: caregiver socio-demographic and health-related data, caregiving activities, knowledge of MS, coping strategies, mood, social support received and care recipient information. Caregiving burden was measured by the ZBI (Zarit Burden Interview). The extent to which the variables explained caregiver burden was analyzed using a hierarchical approach. Results 68% of the caregivers reported a perceived burden of care (ZBI score > 20). Our results show that physical and mental related-health variables are important predictive factors of the care burden, explaining much of the observed variance (40.9%). Conclusion Family caregivers in MS continue to make up the shortfall produce by national health and welfare systems. We highlighted the importance of good physical and mental health in decreasing perceived burden. Working to alleviate psychological distress through mechanisms focus on reducing worries and perceived burden may be a valid approach.

Assessing the impact of climate change on wave conditions, including average and extreme waves, is vital for numerous marine-related activities, industries, coastal vulnerability, and marine habitats. Previous research, primarily on a large scale, has investigated this topic, but its relevance for marginal basins like the Adriatic Sea is limited due to the low resolution of the wave models used and atmospheric forcing. To contribute to filling in the gap, here we implemented a high-resolution model (about 2 km) for the period 1992–2050. The future wave climate is simulated for the RCP8.5 emission scenario. This model, developed within the AdriaClim project, comprises, among others, a high-resolution atmospheric downscaling, a circulation Limited Area Model and a spectral wave model. A comparison of our simulation's results with Copernicus Marine Service wave reanalysis on the historical baseline, confirms its accuracy in reproducing both average wave parameters and 95th percentile values, as well as the seasonal cycle, showing the AdriaClim model's suitability as a source to predict future wave climates in the Adriatic Sea. The projected changes suggest a slight increase in average significant wave height and mean wave period, and a more significant decrease at the 95th percentile, with a relevant variability by location and season, partially aligning with previous studies. This study highlights the potential effect of local climate change in coastal areas and the importance of developing long-term simulation with a downscaled modeling system for regional areas.

BackgroundKawasaki disease (KD), an acute febrile illness and systemic vasculitis, is the leading cause of acquired heart disease in children in industrialized countries. KD leads to the development of coronary artery aneurysms (CAA) in affected children, which may persist for months and even years after the acute phase of the disease. There is an unmet need to characterize the immune and pathological mechanisms of the long-term complications of KD.MethodsWe examined cardiovascular complications in the Lactobacillus casei cell wall extract (LCWE) mouse model of KD-like vasculitis over 4 months. The long-term immune, pathological, and functional changes occurring in cardiovascular lesions were characterized by histological examination, flow cytometric analysis, immunofluorescent staining of cardiovascular tissues, and transthoracic echocardiogram.ResultsCAA and abdominal aorta dilations were detected up to 16 weeks following LCWE injection and initiation of acute vasculitis. We observed alterations in the composition of circulating immune cell profiles, such as increased monocyte frequencies in the acute phase of the disease and higher counts of neutrophils. We determined a positive correlation between circulating neutrophil and inflammatory monocyte counts and the severity of cardiovascular lesions early after LCWE injection. LCWE-induced KD-like vasculitis was associated with myocarditis and myocardial dysfunction, characterized by diminished ejection fraction and left ventricular remodeling, which worsened over time. We observed extensive fibrosis within the inflamed cardiac tissue early in the disease and myocardial fibrosis in later stages.ConclusionOur findings indicate that increased circulating neutrophil counts in the acute phase are a reliable predictor of cardiovascular inflammation severity in LCWE-injected mice. Furthermore, long-term cardiac complications stemming from inflammatory cell infiltrations in the aortic root and coronary arteries, myocardial dysfunction, and myocardial fibrosis persist over long periods and are still detected up to 16 weeks after LCWE injection.

IntroductionBody image concerns related to breast cancer surgery may challenge patients’ quality of life and their treatment outcomes, thus representing a key aspect to be assessed in the psycho-oncological settings. The present longitudinal study is aimed to (1) investigate the association between preoperative body image and postoperative psychological symptoms in breast cancer patients; (2) explore the impact of pre−/post-surgery variation in body image on psychological symptomatology.MethodsN = 72 women undergoing breast cancer surgery were preoperatively screened (T1) using the Body Uneasiness Test (BUT) and were assessed postoperatively (T2) using the Symptom Checklist-90 Revised (SCL-90-R) and re-administered the BUT. Spearman’s correlation was used to investigate the relationship between age, preoperative body image and postoperative psychological symptoms, and variation in body image. To predict post-surgical psychological symptomatology, two separated multiple regression models were used to evaluate preoperative body image and its variation after surgery controlling for covariates (i.e., education; intervention type). P significance was set as 0.05 for all analyses and adjusted for multiple comparisons.ResultsAt T1, anxiety in relation to body image scores emerged as the most frequently experienced psychological symptomatology after surgery (all adjusted p 0.05). Pre-surgery body avoidance was significantly associated with post-intervention psychological symptoms (SOMβ = 0.453, p = 0.0001; DEPβ = 0.507, p = 0.0001; AXβ = 0.459, p = 0.0001; HOSβ = 0.410, p=. 0001). However, increased weight phobia between pre- and post-surgery was statistically associated with increased somatization, anxiety, depression and hostility at T2 (βSOM = 0.439, p = 0.0001; βDEP = 0.454, p = 0.0001; βANX = 0.471, p = 0.0001).DiscussionOverall, pre−/post-intervention body concerns were significantly associated with primary psychological symptoms in breast cancer patients undergoing surgery. Higher levels of body avoidance and weight phobia were significantly associated with the primary psychological dimensions assessed. As body concerns might act as quality-of-life predictors, their evaluation is crucial in fostering patients’ well-being and treatment adherence.

White marble sculpture is a cornerstone of Western art history. Archaeological inquiry, however, has demonstrated that Classical sculpture and its associated architecture were once coloured. The authors examine the Parthenon Sculptures at the British Museum to identify traces of colour and carving on their surfaces. Using close examination and archaeometric techniques, the study shows that the sculptors finished surfaces with textures that reflected specific elements (e.g. skin, wool, linen) and these were then enhanced through the application of colour, including a purple colourant and Egyptian blue. The latter was used extensively to paint elaborate figurative designs on the carved textiles. Despite the complexity of the carved drapery, elaborate ornament was applied to the finish. The findings encourage a reconsideration of the appearance of the Parthenon in the fifth century BC. Keywords: Classical Athens, Parthenon, visible-induced luminescence, XRF, carving, sculpture, colour, Introduction In Ancient Greece, marble sculpture, like architecture, was painted. Although studies of ancient polychromy began at least a couple of centuries ago, the exact appearance of ancient sculpture and [...]

Wildfires pose a significant threat to ecosystems, human lives, and infrastructure worldwide. Traditional wildfire detection and risk assessment methods often suffer from limitations such as delayed detection and low confidence in certain regions. In this paper, we propose a novel computational system based on Machine Learning for wildfire risk assessment using data collected by drones. The system can integrate various sensors to capture spatiotemporal data on environmental factors such as temperature, humidity, and vegetation. By leveraging high-resolution data collected through autonomous drone missions, our system enhances wildfire risk estimation and enables proactive mission planning. Although the system is mainly designed to address wildfire monitoring using drone-collected data, it can be easily adapted to other environmental monitoring applications and other sources of data. We demonstrate the effectiveness of our approach through a comprehensive evaluation and validation process in both simulated and real-world environments. Our work contributes to advancing wildfire monitoring capabilities, improving early detection, and mitigating the impact of wildfires on communities and the environment.

Abstract The laparoscopic approach represents the standard of treatment for renal and adrenal diseases, and its use is increasing even outside referral centres. Although most procedures are routinely performed, intraoperative complications do not occur, and the rate and predictive factors of these complications have not been established. The aim of this study was to evaluate the incidence and type of intraoperative complications and to identify predictive factors in patients undergoing laparoscopic renal and adrenal surgery. This was a cohort, multicentre, international retrospective study. Patients who underwent laparoscopic renal and adrenal surgeries between April 2017 and March 2022 were included in the study. Bivariate analysis was performed using contingency tables and the χ2 test for independent samples to compare qualitative variables and the T test and Mood test for continuous variables. Multivariate analysis was performed using a logistic regression model to obtain adjusted odds ratios. A total of 2374 patients were included in the study. Intraoperative complications were reported for 8.09% of patients who underwent renal surgery, with the most common complications reported being hollow viscus and vascular complications, and for 6.75% of patients who underwent adrenal surgery, with the most common complication reported being parenchymatous viscous complications. Multivariate analysis revealed that both adrenal and renal surgery radiological preoperative factors, such as invasive features during adrenalectomy and the RENAL score during nephrectomy, are predictive factors of intraoperative complications. In contrast to existing data, surgeon experience was not associated with a reduction in the incidence of perioperative complications.

Reading essays, articles, interviews and occasional writings, written between the 60s and the 80s, a substantially constant picture is drawn of what Sciascia says about his region, its identity, the history that made it as it is. At the beginning of the period under review, at least in part, there seems to be some optimism about the possibility of achieving positive change. In any case, the writer only made minor changes to the image of a negative condition unaltered through the centuries, which in the face of the disappointments for the present, in turn extends the shadows of him even at the moment lived, already in the mid-60s. Despite the perplexities sometimes declared as to the fact that the Sicilian identity has been able to remain unchanged for centuries, the writer accepts it and sometimes gives in to some clichés.