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Objective: To report oncological outcomes after thulium-yttrium-aluminum-garnet (Tm:YAG) laser ablation for penile cancer patients., Materials and Methods: We retrospectively analyzed 71 patients with ≤cT1 penile cancer (2013-2022). All patients underwent Tm:YAG ablation with a RevoLix 200W continuous-wave laser. First, Kaplan-Meier plots and multivariable Cox regression models tested local tumor recurrence rates. Second, Kaplan-Meier plots tested progression-free survival (≥T3 and/or N1-3 and/or M1)., Results: Median (interquartile range) follow-up time was 38 (22-58) months. Overall, 33 (50.5%) patients experienced local tumor recurrence. Specifically, 19 (29%) vs 9 (14%) vs 5 (7.5%) patients had 1 vs 2 vs 3 recurrences over time. In multivariable Cox regression models, a trend for higher recurrence rates was observed for G3 tumors (hazard ratio:6.1; P = .05), relative to G1. During follow-up, 12 (18.5%) vs 4 (6.0%) vs 2 (3.0%) men were retreated with 1 vs 2 vs 3 Tm:YAG laser ablations. Moreover, 11 (17.0%) and 3 (4.5%) patients underwent glansectomy and partial/total penile amputation. Last, 5 (7.5%) patients experienced disease progression. Specifically, TNM stage at the time of disease progression was: (1) pT3N0; (2) pT2N2; (3) pTxN3; (4) pT1N1 and (5) pT3N3, respectively., Conclusion: Tm:YAG laser ablation provides similar oncological results as those observed by other penile-sparing surgery procedures. In consequence, Tm:YAG laser ablation should be considered a valid alternative for treating selected penile cancer patients., Competing Interests: Declaration of Competing Interest None., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: Prostate cancer (PCa) is the most frequently diagnosed cancer in men worldwide, affecting around 1.4 million individuals. Current PCa diagnosis relies on histological analysis of prostate biopsy samples, an activity that is both time-consuming and prone to observer bias. Previous studies have demonstrated that immunostaining of cytokeratin, p63, and racemase can significantly improve the sensitivity and the specificity of PCa detection compared to traditional H&E staining., Methods: This study introduces a novel approach that combines diagnosis-specific immunohistochemical (IHC) staining and deep learning techniques to provide reliable stratification of prostate glands. Our approach leverages a customized segmentation network, called K-PPM, that incorporates adaptive kernels and multiscale feature integration to enhance the functional information of IHC. To address the high class-imbalance problem in the dataset, we propose a weighted adaptive patch-extraction and specific-class kernel update., Results: Our system achieved noteworthy results, with a mean Dice Score Coefficient of 90.36% and a mean absolute error of 1.64 % in specific-class gland quantification on whole slides. These findings demonstrate the potential of our system as a valuable support tool for pathologists, reducing workload and decreasing diagnostic inter-observer variability., Conclusions: Our study presents innovative approaches that have broad applicability to other digital pathology areas beyond PCa diagnosis. As a fully automated system, this model can serve as a framework for improving the histological and IHC diagnosis of other types of cancer., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

In accordance with the World Health Organization, one dose of yellow fever vaccine may guarantee protection lifelong in healthy adults. However, relatively little information is still available from ad hoc studies. We evaluated the persistence of neutralizing antibodies, which are considered to be an immune correlate of protection, in a large number of military personnel vaccinated up to 47 years before. Overall, 322 individuals were studied. The median time from vaccination to blood collection for neutralizing antibody evaluation was 9 years, ranging from <1 to 47 years. Of the 322 participants, 319 had neutralizing antibodies (99.1 %). The highest median PRNT 50 value was observed in those vaccinated ≤1 year before (median PRNT 50  = 320). In conclusion, our study confirms on a larger scale that, in healthy adults, neutralizing antibodies may persist as long as 47 years after a single yellow fever vaccines dose., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

A high-quality dataset of 3289 complete SARS-CoV-2 genomes collected in Europe and European Economic Area (EAA) in the early phase of the first wave of the pandemic was analyzed. Among all single nucleotide mutations, 41 had a frequency ≥ 1%, and the phylogenetic analysis showed at least 6 clusters with a specific mutational profile. These clusters were differentially distributed in the EU/EEA, showing a statistically significant association with the geographic origin. The analysis highlighted that the mutations C 14408 T and C 14805 T played an important role in clusters selection and further virus spread. Moreover, the molecular analysis suggests that the SARS-CoV-2 strain responsible for the first Italian confirmed COVID-19 case was already circulating outside the country., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

In kidney transplantations, pathologists evaluate the architecture of both glomeruli, interstitium and tubules to assess the nephron status. An accurate assessment of glomerulosclerosis and tubular atrophy is crucial for determining kidney acceptance, which is currently based on the pathologists' histological evaluations on renal biopsies in addition to clinical data. In this work, we present an automated algorithm, called RENTAG (Robust EvaluatioN of Tubular Atrophy & Glomerulosclerosis), for the segmentation and classification of glomerular and tubular structures in histopathological images. The proposed novel strategy combines the accuracy of a level-set with the semantic segmentation of convolutional neural networks to detect the glomeruli and tubules contours. In the TEST set, our method exhibited excellent performance in both glomeruli (dice score: 0.9529) and tubule (dice score: 0.9174) detection and outperformed all the compared methods. To the best of our knowledge, the RENTAG algorithm is the first fully automated method capable of quantifying glomerulosclerosis and tubular atrophy in digital histological images. The developed software can be employed for the analysis of pre-transplantation biopsies to support the pathologists' diagnostic activity., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Thyroid is a small gland situated at the anterior side of the neck and one of the largest glands of the endocrine system. The abrupt cell growth or malignancy in the thyroid gland may cause thyroid cancer. Ultrasound images distinctly represent benign and malignant lesions, but accuracy may be poor due to subjective interpretation. Computer Aided Diagnosis (CAD) can minimize the errors created due to subjective interpretation and assists to make fast accurate diagnosis. In this work, fusion of Spatial Gray Level Dependence Features (SGLDF) and fractal textures are used to decipher the intrinsic structure of benign and malignant thyroid lesions. These features are subjected to graph based Marginal Fisher Analysis (MFA) to reduce the number of features. The reduced features are subjected to various ranking methods and classifiers. We have achieved an average accuracy, sensitivity and specificity of 97.52%, 90.32% and 98.57% respectively using Support Vector Machine (SVM) classifier. The achieved maximum Area Under Curve (AUC) is 0.9445. Finally, Thyroid Clinical Risk Index (TCRI) a single number is developed using two MFA features to discriminate the two classes. This prototype system is ready to be tested with huge diverse database., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Turbulent-like flows without cycle-to-cycle variations are more frequently being reported in studies of cardiovascular flows. The associated stimuli might be of mechanobiological relevance, but how to quantify them objectively is not obvious. Classical Reynolds decomposition, where the flow is separated into mean and fluctuating velocity components, is not applicable as the phase-average is zero. We therefore expanded on established techniques and present the idea, analogous to Reynolds decomposition, to decompose a flow with transient instabilities into low- versus high frequency components, respectively, to discriminate flow instabilities from the underlying cardiac pulsatility. Transient wall shear stress and velocity signals derived from computational fluid dynamic simulations were transferred to the frequency domain. A high-pass filter was applied to subtract the 99% most-energy-containing frequencies, which gave a cut-off frequency of 25Hz. We introduce here the spectral power index, and compute the fluctuating kinetic energy, based on the high-pass filtered velocity components, both being frequency-based operators. The efficacy was evaluated in an aneurysm model for multiple flow rates demonstrating transition to turbulent-like flows. The frequency-based operators were found to better correlate with the qualitatively observed flow instabilities compared to conventional descriptors, like time-averaged wall shear stress or oscillatory shear index. We demonstrate how the high frequencies beyond the physiological range could be analyzed and/or transferred back to the time domain for quantification and visualization purposes. We have introduced general frequency-based operators, easily extendable to other cardiovascular territories based on a posteriori heuristic filtering that allows for separation, isolation, and quantification of cycle-invariant turbulent-like flows., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Non-lypolitic esterases are carboxylester hydrolases with preference for the hydrolysis of water-soluble esters bearing short-chain acyl residues. The potential of esterases as enantioselective biocatalysts has enlarged in the last few years due to the progresses achieved in different areas, such as screening methodologies, overproduction of recombinant esterases, structural information useful for understanding the rational behind enantioselectivity, and efficient methods in protein engineering. Contributions of these complementary know-hows to the development of new robust enantioselective esterases are critically discussed in this review., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Accurate intima-media thickness (IMT) measurement of the carotid artery from minimal plaque ultrasound images is a relevant clinical need, since IMT increase is related to the progression of atherosclerosis. In this paper, we describe a novel dual snake-based model for the high-performance carotid IMT measurement, called Carotid Measurement Using Dual Snakes (CMUDS). Snakes (which are deformable contours) adapt to the lumen-intima (LI) and media-adventitia (MA) interfaces, thus enabling the IMT computation as distance between the LI and MA snakes. However, traditional snakes might be unable to maintain a correct distance and in some spatial location along the artery, it might even collapse between them or diverge. The technical improvement of this work is the definition of a dual snake-based constrained system, which prevents the LI and MA snakes from collapsing or bleeding, thus optimizing the IMT estimation. The CMUDS system consists of two parametric models automatically initialized using the far adventitia border which we automatically traced by using a previously developed multi-resolution approach. The dual snakes evolve simultaneously and are constrained by the distances between them, ensuring the regularization of LI/MA topology. We benchmarked our automated CMUDS with the previous conventional semi-automated snake system called Carotid Measurement Using Single Snake (CMUSS). Two independent readers manually traced the LIMA boundaries of a multi-institutional, multi-ethnic, and multi-scanner database of 665 CCA longitudinal 2D images. We evaluated our system performance by comparing it with the gold standard as traced by clinical readers. CMUDS and CMUSS correctly processed 100% of the 665 images. Comparing the performance with respect to the two readers, our automatically measured IMT was on average very close to that of the two readers (IMT measurement biases for CMUSS was equal to -0.011±0.329mm and -0.045±0.317mm, respectively, while for CMUDS, it was 0.030±0.284mm and -0.004±0.273mm, respectively). The Figure-of-Merit of the system was 98.5% and 94.4% for CMUSS, while 96.0% and 99.6% for CMUDS, respectively. Results showed that the dual-snake system CMUDS reduced the IMT measurement error accuracy (Wilcoxon, p<0.02) and the IMT error variability (Fisher, p<3×10(-2)). We propose the CMUDS technique for use in large multi-centric studies, where the need for a standard, accurate, and automated IMT measurement technique is required., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Metabotropic glutamate receptors (mGluR) can control neuronal excitability by modulating several ionic channels. In hippocampal pyramidal cells, groups I/II mGluR are located extrasynaptically, suggesting that their endogenous activation is dependent on the glutamate clearance rate and therefore on excitatory amino-acid transporters (EAAT) efficiency. Deficiency of glutamate uptake can generate seizures in rodents and has been suggested as a mechanism of seizure generation in some human epileptic syndromes. However, the cellular mechanisms linking EAAT dysfunction and pathological cortical activities remain elusive. Here, we investigate the possible role of mGluR on paroxysmal burst of multiple unit activities (MUA) generated in the CA1 region of developing hippocampal slices using an EAAT inhibitor, TBOA. These bursts are generated by a synaptic release of glutamate and involve extrasynaptic NMDA receptors (NMDAR) activated by transmitter spillover. Here, we show that postsynaptic mGluR (groups I/II) are tonically activated by the rise in ambient glutamate concentration after EAAT inhibition and strongly contribute to paroxysmal burst genesis. The inhibition of mGluR with broad spectrum antagonists or addition of a glutamate scavenger strongly reduced the occurrence of paroxysmal burst and the frequency/number of MUA during the burst. Moreover, this endogenous activation of groups I/II mGluR leads to (i) the reduction of the slow afterhyperpolarization current (I(sAHP)), increasing the firing pattern of pyramidal cells, and (ii) the potentiation of extrasynaptic NMDAR-mediated responses, enabling glutamate spillover to generate a suprathreshold depolarization for several seconds. Our data show that an insufficient buffering of extracellular glutamate enables a cross talk between groups I/II mGluR and NMDAR, which, combined with a decrease of I(sAHP), leads to the hyperexcitability of the hippocampal network, facilitating the genesis of epileptical-like activity in response to glutamate release. These findings highlight the importance of the control exerted by EAAT on mGluR., (Copyright © 2012 IBRO. Published by Elsevier Ltd. All rights reserved.)

Ultrasound-based thyroid nodule characterization into benign and malignant types is limited by subjective interpretations. This paper presents a Computer Aided Diagnostic (CAD) technique that would present more objective and accurate classification and further would offer the physician a valuable second opinion. In this paradigm, we first extracted the features that quantify the local changes in the texture characteristics of the ultrasound off-line training images from both benign and malignant nodules. These features include: Fractal Dimension (FD), Local Binary Pattern (LBP), Fourier Spectrum Descriptor (FS), and Laws Texture Energy (LTE). The resulting feature vectors were used to build seven different classifiers: Support Vector Machine (SVM), Decision Tree (DT), Sugeno Fuzzy, Gaussian Mixture Model (GMM), K-Nearest Neighbor (KNN), Radial Basis Probabilistic Neural Network (RBPNN), and Naive Bayes Classifier (NBC). Subsequently, the feature vector-classifier combination that results in the maximum classification accuracy was used to predict the class of a new on-line test thyroid ultrasound image. Two data sets with 3D Contrast-Enhanced Ultrasound (CEUS) and 3D High Resolution Ultrasound (HRUS) images of 20 nodules (10 benign and 10 malignant) were used. Fine needle aspiration biopsy and histology results were used to confirm malignancy. Our results show that a combination of texture features coupled with SVM or Fuzzy classifiers resulted in 100% accuracy for the HRUS dataset, while GMM classifier resulted in 98.1% accuracy for the CEUS dataset. Finally, for each dataset, we have proposed a novel integrated index called Thyroid Malignancy Index (TMI) using the combination of FD, LBP, LTE texture features, to diagnose benign or malignant nodules. This index can help clinicians to make a more objective differentiation of benign/malignant thyroid lesions. We have compared and benchmarked the system with existing methods., (Copyright © 2011 Elsevier B.V. All rights reserved.)