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Background: A strong association between aortic valve sclerosis (AVSc), the earliest manifestation of calcific aortic valve disease, and atherosclerosis exists. The aim of the study was to evaluate the predictive capabilities of AVSc on long-term all-cause mortality, in patients requiring carotid endarterectomy (CEA).Methods and Results: 806 consecutive CEA patients were enrolled. Preoperative echocardiography was used to assess AVSc. Computed tomography angiography was applied for plaque characterization. Kaplan-Meier curves, Cox linear regression, and area under the receiving operator characteristic (AUC) curve analyses were used to evaluate the predictive capability of AVSc. Overall, 348 of 541 patients had AVSc (64%). Age, diabetes, and estimated glomerular filtration rate (eGFR) were associated with AVSc. In the 5-year follow-up, AVSc group had a mortality rate of 16.7% while in no-AVSc group was 7.8%. Independent predictors of all-cause mortality were age, sex, eGFR, left ventricular ejection fraction, and AVSc. After adjustments, AVSc was associated with a significant increase in all-cause mortality risk (hazard ratio, HR = 1.9; 95%CI: 1.04–3.54; p = 0.038). We stratify our cohort based on carotid atheromatous plaque-type: soft, calcified, and mixed-fibrotic. In patients with mixed-fibrotic plaques, the mortality rate of AVSc patients was 15.5% compared to 2.4% in no-AVSc patients. In this group, AVSc was associated with an increased long-term all-cause mortality risk with an adjusted HR of 12.8 (95%CI: 1.71–96.35; p = 0.013), and the AUC, combing eGFR and AVSc was 0.77 (p < 0.001).Conclusions: Our findings indicate that AVSc together with eGFR may be used to improve long-term risk stratification of patients undergoing CEA surgery.

The rapid growth of drone use in urban areas has prompted authorities to review airspace regulations, forcing drone manufacturers to anticipate and reduce the noise emissions during the design stage. Additionally, micro air vehicles (MAVs) are designed to be aerodynamically efficient, allowing them to fly farther, longer and safer. In this study, a steady aerodynamic code and an acoustic propagator based on the non-linear vortex lattice method (NVLM) and Farassat's formulation-1A of the Ffowcs Williams and Hawkings (FW-H) acoustic analogy, respectively, are coupled with pymoo, a python-based optimization framework. This tool is used to perform a multi-objective (noise and aerodynamic efficiency) optimization of a 20 cm diameter two-bladed rotor under hovering conditions. From the set of optimized results, (i.e., the Pareto front), three different rotors are 3D-printed using a stereolithography (SLA) technique and tested in an anechoic room. Here, an array of far-field microphones captures the acoustic radiation and directivity of the rotor, while a balance measures the aerodynamic performance. Both the aerodynamic and aeroacoustic performance of the three different rotors, in line with what has been predicted by the numerical codes, are compared and guidelines for the design of aerodynamically and aeroacoustically efficient MAV rotors are extracted. [ABSTRACT FROM AUTHOR]

Objective: Oral lesions received increased attention as likely new signs or secondary manifestations of COVID‐19. Therefore, we clinically examined oral cavity of patients with COVID‐19 and investigated oral lesions and patient comorbidities as possible risk factors of COVID‐19 disease outcome. Methods: From January to March 2022, a prospective study was conducted by recruiting all COVID‐19 patients admitted to the Intensive Care Unit and Respiratory Intensive Care Unit of Maxi‐Emergencies Hospital in Bari, Italy. Results: From the enrolled 103 COVID‐19 patients, 46.6% were females and 53.4% were males. Findings show that risk of presenting with severe COVID‐19 disease was higher in patients who developed oral lesions related to COVID‐19 than those with no oral lesions (RR = 7.998, p =.002). Next, patients with concomitant autoimmune diseases were at higher risk of a negative COVID‐19 disease outcome than those without comorbidities (OR = 8.838, p =.026). Conclusions: COVID‐19‐related lesions of oral mucosa should not be ignored as they can be early and easily detectable signs of severe COVID‐19 disease condition, thus, serving as a prevention measure for any potential unfortunate event. Findings of this study, without implying causation, offer a direction for future investigations that aim to confirm the presence of specific oral lesions in COVID‐19 patients as signs of severe disease progression. [ABSTRACT FROM AUTHOR]

Background. Gastrointestinal (GI) cancers are one of the most relevant causes of death globally, frequently associated with poor dietary patterns. The Mediterranean Diet (MedDiet) contributes to cancer prevention. To assess adherence to MedDiet, our research group validated a new score, the Chrono Med Diet Score (CMDS), that captures increased visceral adiposity. Methods. We enrolled 401 subjects who underwent an evaluation for metabolic diseases and specific screening procedures according to current guidelines and were asked to answer CMDS. A total of 71 new cancer cases were recorded, including 40 GI and 31 non-gastrointestinal (NON-GI) cancers. Results. We found that CMDS was reduced in subjects who were diagnosed with cancers. Patients who reported a CMDS score of 12 or less had an over three times increased risk of being diagnosed with GI cancers and presented increased waist circumference and triglycerides and reduced HDL cholesterol compared to adherent subjects. Conclusions. Low CMDS values capture the risk for cancer diagnosis, especially for GI cancers. Thus, CMDS, along with waist circumference, can be considered as a bona fide marker for increased risk of cancer, requiring anticipated screening procedures for the detection of premalignant and early stage GI cancers in patients with low adherence to MedDiet. [ABSTRACT FROM AUTHOR]

The Gatenby-Gawlinski model for the description of cancer invasion is taken into account with the aim of investigating its structural aspects. From a phenomenological point of view, that model is inspired by the Warburg effect and relies on the acid-mediated invasion hypothesis. By means of both an analytical and numerical approach, on the heels of the results available in the literature, the main purpose consists in paving the way for improving the understanding of the mechanisms the model is ruled by. We adopt a suitable numerical strategy based on a finite volume approximation and provide a space-averaged propagation speed estimate as a tool to investigate the phenomenon of traveling fronts. Simulations are performed and realistic evidence emerging from the experiments is highlighted. Afterwards, we make reasonable assumptions for justifying some system reductions leading to a simplified version and discuss the results without neglecting natural bonds with the whole system. Finally, the multidimensional environment is explored with emphasis on some important qualitative aspects strongly linked to experimental observations.

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.

Circulating microRNAs (miRNA) have been proposed as specific biomarkers for several diseases. Quantitative Real-Time PCR (RT-qPCR) is the gold standard technique currently used to evaluate miRNAs expression from different sources. In the last few years, digital PCR (dPCR) emerged as a complementary and accurate detection method. When dealing with gene expression, the first and most delicate step is nucleic-acid isolation. However, all currently available protocols for RNA extraction suffer from the variable loss of RNA species due to the chemicals and number of steps involved, from sample lysis to nucleic acid elution. Here, we evaluated a new process for the detection of circulating miRNAs, consisting of sample lysis followed by direct evaluation by dPCR in plasma from healthy donors and in the cardiovascular setting. Our results showed that dPCR is able to detect, with high accuracy, low-copy-number as well as highly expressed miRNAs in human plasma samples without the need for RNA extraction. Moreover, we assessed a known myocardial infarction-related miR-133a in acute myocardial infarct patients vs. healthy subjects. In conclusion, our results show the suitability of the extraction-free quantification of circulating miRNAs as disease markers by direct dPCR.

The metabolic syndrome (MetS) is a cluster of cardiovascular risk factors characterised by central obesity, atherogenic dyslipidaemia, and changes in the circulating lipidome; the underlying mechanisms that lead to this lipid remodelling have only been partially elucidated. This study used an integrated “omics” approach (untargeted whole serum lipidomics, targeted proteomics, and lipoprotein lipidomics) to study lipoprotein remodelling and HDL composition in subjects with central obesity diagnosed with MetS (vs. controls). Compared with healthy subjects, MetS patients showed higher free fatty acids, diglycerides, phosphatidylcholines, and triglycerides, particularly those enriched in products of de novo lipogenesis. On the other hand, the “lysophosphatidylcholines to phosphatidylcholines” and “cholesteryl ester to free cholesterol” ratios were reduced, pointing to a lower activity of lecithin cholesterol acyltransferase (LCAT) in MetS; LCAT activity (directly measured and predicted by lipidomic ratios) was positively correlated with high-density lipoprotein cholesterol (HDL-C) and negatively correlated with body mass index (BMI) and insulin resistance. Moreover, many phosphatidylcholines and sphingomyelins were significantly lower in the HDL of MetS patients and strongly correlated with BMI and clinical metabolic parameters. These results suggest that MetS is associated with an impairment of phospholipid metabolism in HDL, partially led by LCAT, and associated with obesity and underlying insulin resistance. This study proposes a candidate strategy to use integrated “omics” approaches to gain mechanistic insights into lipoprotein remodelling, thus deepening the knowledge regarding the molecular basis of the association between MetS and atherosclerosis.

The ability to identify ovarian cancer (OC) at its earliest stages remains a challenge. The patients present an advanced stage at diagnosis. This heterogeneous disease has distinguishable etiology and molecular biology. Next-generation sequencing changed clinical diagnostic testing, allowing assessment of multiple genes, simultaneously, in a faster and cheaper manner than sequential single gene analysis. Technologies of proteomics, such as mass spectrometry (MS) and protein array analysis, have advanced the dissection of the underlying molecular signaling events and the proteomic characterization of OC. Proteomics analysis of OC, as well as their adaptive responses to therapy, can uncover new therapeutic choices, which can reduce the emergence of drug resistance and potentially improve patient outcomes. There is an urgent need to better understand how the genomic and epigenomic heterogeneity intrinsic to OC is reflected at the protein level, and how this information could potentially lead to prolonged survival.