Diagnostic Applications of Intraoral Scanners: A Systematic Review.

In addition to their recognized value for obtaining 3D digital dental models, intraoral scanners (IOSs) have recently been proven to be promising tools for oral health diagnostics. In this work, the most recent literature on IOSs was reviewed with a focus on their applications as detection systems of oral cavity pathologies. Those applications of IOSs falling in the general area of detection systems for oral health diagnostics (e.g., caries, dental wear, periodontal diseases, oral cancer) were included, while excluding those works mainly focused on 3D dental model reconstruction for implantology, orthodontics, or prosthodontics. Three major scientific databases, namely Scopus, PubMed, and Web of Science, were searched and explored by three independent reviewers. The synthesis and analysis of the studies was carried out by considering the type and technical features of the IOS, the study objectives, and the specific diagnostic applications. From the synthesis of the twenty-five included studies, the main diagnostic fields where IOS technology applies were highlighted, ranging from the detection of tooth wear and caries to the diagnosis of plaques, periodontal defects, and other complications. This shows how additional diagnostic information can be obtained by combining the IOS technology with other radiographic techniques. Despite some promising results, the clinical evidence regarding the use of IOSs as oral health probes is still limited, and further efforts are needed to validate the diagnostic potential of IOSs over conventional tools.

Machine Learning Approaches with Textural Features to Calculate Breast Density on Mammography.

BACKGROUND: breast cancer (BC) is the world's most prevalent cancer in the female population, with 2.3 million new cases diagnosed worldwide in 2020. The great efforts made to set screening campaigns, early detection programs, and increasingly targeted treatments led to significant improvement in patients' survival. The Full-Field Digital Mammograph (FFDM) is considered the gold standard method for the early diagnosis of BC. From several previous studies, it has emerged that breast density (BD) is a risk factor in the development of BC, affecting the periodicity of screening plans present today at an international level. OBJECTIVE: in this study, the focus is the development of mammographic image processing techniques that allow the extraction of indicators derived from textural patterns of the mammary parenchyma indicative of BD risk factors. METHODS: a total of 168 patients were enrolled in the internal training and test set while a total of 51 patients were enrolled to compose the external validation cohort. Different Machine Learning (ML) techniques have been employed to classify breasts based on the values of the tissue density. Textural features were extracted only from breast parenchyma with which to train classifiers, thanks to the aid of ML algorithms. RESULTS: the accuracy of different tested classifiers varied between 74.15% and 93.55%. The best results were reached by a Support Vector Machine (accuracy of 93.55% and a percentage of true positives and negatives equal to TPP = 94.44% and TNP = 92.31%). The best accuracy was not influenced by the choice of the features selection approach. Considering the external validation cohort, the SVM, as the best classifier with the 7 features selected by a wrapper method, showed an accuracy of 0.95, a sensitivity of 0.96, and a specificity of 0.90. CONCLUSIONS: our preliminary results showed that the Radiomics analysis and ML approach allow us to objectively identify BD.

Comparing Two Approaches for Thyroidectomy: A Health Technology Assessment through DMAIC Cycle.

Total thyroidectomy is very common in endocrine surgery and the haemostasis can be obtained in different ways across surgery; recently, some devices have been developed to support this surgical phase. In this paper, a health technology assessment is conducted through the define, measure, analyse, improve, and control cycle of the Six Sigma methodology to compare traditional total thyroidectomy with the surgical operation performed through a new device in an overall population of 104 patients. Length of hospital stay, drain output, and time for surgery were considered the critical to qualities in order to compare the surgical approaches which can be considered equal regarding the organizational, ethical, and security impact. Statistical tests (Kolmogorov-Smirnov, t test, ANOVA, Mann-Whitney, and Kruskal-Wallis tests) and visual management diagrams were employed to compare the approaches, but no statistically significant difference was found between them. Considering these results, this study shows that the introduction of the device to perform total thyroidectomy does not guarantee appreciable clinical advantages. A cost analysis to quantify the economic impact of the device into the practice could be a future development. Healthy policy leaders and clinicians who are requested to make decisions regarding the supply of biomedical technologies could benefit from this research.

A machine learning-based approach to directly compare the diagnostic accuracy of myocardial perfusion imaging by conventional and cadmium-zinc telluride SPECT.

BACKGROUND: We evaluated the performance of conventional (C) single-photon emission computed tomography (SPECT) and cadmium-zinc-telluride (CZT)-SPECT in a large cohort of patients with suspected or known coronary artery disease (CAD) and compared the diagnostic accuracy of the two systems using machine learning (ML) algorithms. METHODS AND RESULTS: A total of 517 consecutive patients underwent stress myocardial perfusion imaging (MPI) by both C-SPECT and CZT-SPECT. In the overall population, an excellent correlation between stress MPI data and left ventricular (LV) functional parameters measured by C-SPECT and by CZT-SPECT was observed (all P < .001). ML analysis performed through the implementation of random forest (RF) and k-nearest neighbors (NN) algorithms proved that CZT-SPECT has greater accuracy than C-SPECT in detecting CAD. For both algorithms, the sensitivity of CZT-SPECT (96% for RF and 60% for k-NN) was greater than that of C-SPECT (88% for RF and 53% for k-NN). CONCLUSIONS: MPI data and LV functional parameters obtained by CZT-SPECT are highly reproducible and provide good correlation with those obtained by C-SPECT. ML approach showed that the accuracy and sensitivity of CZT-SPECT is greater than C-SPECT in detecting CAD.

A Combined Radiomics and Machine Learning Approach to Distinguish Clinically Significant Prostate Lesions on a Publicly Available MRI Dataset.

Although prostate cancer is one of the most common causes of mortality and morbidity in advancing-age males, early diagnosis improves prognosis and modifies the therapy of choice. The aim of this study was the evaluation of a combined radiomics and machine learning approach on a publicly available dataset in order to distinguish a clinically significant from a clinically non-significant prostate lesion. A total of 299 prostate lesions were included in the analysis. A univariate statistical analysis was performed to prove the goodness of the 60 extracted radiomic features in distinguishing prostate lesions. Then, a 10-fold cross-validation was used to train and test some models and the evaluation metrics were calculated; finally, a hold-out was performed and a wrapper feature selection was applied. The employed algorithms were Naïve bayes, K nearest neighbour and some tree-based ones. The tree-based algorithms achieved the highest evaluation metrics, with accuracies over 80%, and area-under-the-curve receiver-operating characteristics below 0.80. Combined machine learning algorithms and radiomics based on clinical, routine, multiparametric, magnetic-resonance imaging were demonstrated to be a useful tool in prostate cancer stratification.

Comparing the Prognostic Value of Stress Myocardial Perfusion Imaging by Conventional and Cadmium-Zinc Telluride Single-Photon Emission Computed Tomography through a Machine Learning Approach.

We compared the prognostic value of myocardial perfusion imaging (MPI) by conventional- (C-) single-photon emission computed tomography (SPECT) and cadmium-zinc-telluride- (CZT-) SPECT in a cohort of patients with suspected or known coronary artery disease (CAD) using machine learning (ML) algorithms. A total of 453 consecutive patients underwent stress MPI by both C-SPECT and CZT-SPECT. The outcome was a composite end point of all-cause death, cardiac death, nonfatal myocardial infarction, or coronary revascularization procedures whichever occurred first. ML analysis performed through the implementation of random forest (RF) and k-nearest neighbors (KNN) algorithms proved that CZT-SPECT has greater accuracy than C-SPECT in detecting CAD. For both algorithms, the sensitivity of CZT-SPECT (96% for RF and 60% for KNN) was greater than that of C-SPECT (88% for RF and 53% for KNN). A preliminary univariate analysis was performed through Mann-Whitney tests separately on the features of each camera in order to understand which ones could distinguish patients who will experience an adverse event from those who will not. Then, a machine learning analysis was performed by using Matlab (v. 2019b). Tree, KNN, support vector machine (SVM), Naïve Bayes, and RF were implemented twice: first, the analysis was performed on the as-is dataset; then, since the dataset was imbalanced (patients experiencing an adverse event were lower than the others), the analysis was performed again after balancing the classes through the Synthetic Minority Oversampling Technique. According to KNN and SVM with and without balancing the classes, the accuracy (p value = 0.02 and p value = 0.01) and recall (p value = 0.001 and p value = 0.03) of the CZT-SPECT were greater than those obtained by C-SPECT in a statistically significant way. ML approach showed that although the prognostic value of stress MPI by C-SPECT and CZT-SPECT is comparable, CZT-SPECT seems to have higher accuracy and recall.

A Health Technology Assessment in Maxillofacial Cancer Surgery by Using the Six Sigma Methodology.

Squamous cell carcinoma represents the most common cancer affecting the oral cavity. At the University of Naples "Federico II", two different antibiotic protocols were used in patients undergoing oral mucosa cancer surgery from 2006 to 2018. From 2011, there was a shift; the combination of Cefazolin plus Clindamycin as a postoperative prophylactic protocol was chosen. In this paper, a health technology assessment (HTA) is performed by using the Six Sigma and DMAIC (Define, Measure, Analyse, Improve, Control) cycle in order to compare the performance of the antibiotic protocols according to the length of hospital stay (LOS). The data (13 variables) of two groups were collected and analysed; overall, 136 patients were involved. The American Society of Anaesthesiologist score, use of lymphadenectomy or tracheotomy and the presence of infections influenced LOS significantly (p-value < 0.05) in both groups. Then, the groups were compared: the overall difference between LOS of the groups was not statistically significant, but some insights were provided by comparing the LOS of the groups according to each variable. In conclusion, in light of the insights provided by this study regarding the comparison of two antibiotic protocols, the utilization of DMAIC cycle and Six Sigma tools to perform HTA studies could be considered in future research.

Application of DMAIC Cycle and Modeling as Tools for Health Technology Assessment in a University Hospital.

BACKGROUND: The Health Technology Assessment (HTA) is used to evaluate health services, manage healthcare processes more efficiently, and compare medical technologies. The aim of this paper is to carry out an HTA study that compares two pharmacological therapies and provides the clinicians with two models to predict the length of hospital stay (LOS) of patients undergoing oral cavity cancer surgery on the bone tissue. METHODS: The six Sigma method was used as a tool of HTA; it is a technique of quality management and process improvement that combines the use of statistics with a five-step procedure: "Define, Measure, Analyze, Improve, Control" referred to in the acronym DMAIC. Subsequently, multiple linear regression has been used to create two models. Two groups of patients were analyzed: 45 were treated with ceftriaxone while 48 were treated with the combination of cefazolin and clindamycin. RESULTS: A reduction of the overall mean LOS of patients undergoing oral cavity cancer surgery on bone was observed of 40.9% in the group treated with ceftriaxone. Its reduction was observed in all the variables of the ceftriaxone group. The best results are obtained in younger patients (-54.1%) and in patients with low oral hygiene (-52.4%) treated. The regression results showed that the best LOS predictors for cefazolin/clindamycin are ASA score and flap while for ceftriaxone, in addition to these two, oral hygiene and lymphadenectomy are the best predictors. In addition, the adjusted R squared showed that the variables considered explain most of the variance of LOS. CONCLUSION: SS methodology, used as an HTA tool, allowed us to understand the performance of the antibiotics and provided variables that mostly influence postoperative LOS. The obtained models can improve the outcome of patients, reducing the postoperative LOS and the relative costs, consequently increasing patient safety, and improving the quality of care provided.

A Six Sigma DMAIC methodology as a support tool for Health Technology Assessment of two antibiotics.

Health Technology Assessment (HTA) and Six Sigma (SS) have largely proved their reliability in the healthcare context. The former focuses on the assessment of health technologies to be introduced in a healthcare system. The latter deals with the improvement of the quality of services, reducing errors and variability in the healthcare processes. Both the approaches demand a detailed analysis, evidence-based decisions, and efficient control plans. In this paper, the SS is applied as a support tool for HTA of two antibiotics with the final aim of assessing their clinical and organizational impact in terms of postoperative Length Of Stay (LOS) for patients undergoing tongue cancer surgery. More specifically, the SS has been implemented through its main tool, namely the DMAIC (Define, Measure, Analyse, Improve, Control) cycle. Moreover, within the DMAIC cycle, a modelling approach based on a multiple linear regression analysis technique is introduced, in the Control phase, to add complementary information and confirm the results obtained by the statistical analysis performed within the other phases of the SS DMAIC. The obtained results show that the proposed methodology is effective to determine the clinical and organizational impact of each of the examined antibiotics, when LOS is taken as a measure of performance, and guide the decision-making process. Furthermore, our study provides a systematic procedure which, properly combining different and well-assessed tools available in the literature, demonstrated to be a useful guidance for choosing the right treatment based on the available data in the specific circumstance.

Vascular growth factors as potential new treatment in cardiorenal syndrome in diabetes.

BACKGROUND: Cardiorenal syndrome in diabetes is characterised by alterations of the cardiovascular system paralleled by kidney disease with progressive renal function decline. In diabetes, chronic metabolic and haemodynamic perturbations drive endothelial dysfunction, inflammation, oxidative stress and progressive tissue fibrosis which, in turn, lead to heart and renal anatomo-functional damage. In physiology, vascular growth factors have been implicated in vascular homeostasis; their imbalance, in disease setting such as diabetes, leads to vascular dysfunction and cardiorenal damage. AIMS: To define the role of vascular growth factors and angiopoietins in cardiorenal syndrome. MATERIAL AND METHODS: We will focus on the two most studied vascular growth factors, vascular endothelial growth factor (VEGF) and angiopoietins (Angpt). The balance and crosstalk between these growth factors are important in organ development and in the maintenance of a healthy vasculature, heart and kidney. The observed alterations in expression/function of these vascular growth factors, as seen in diabetes, are a protective response against external perturbations. RESULTS: The chronic insults driving diabetes-mediated cardiorenal damage results in a paradoxical situation, whereby the vascular growth factors imbalance becomes a mechanism of disease. Studies have explored the possibility of modulating the expression/action of vascular growth factors to improve disease outcome. Experimental work has been conducted in animals and has been gradually translated in humans. DISCUSSION: Difficulties have been encountered especially when considering the magnitude, timing and duration of interventions targeting a selective vascular growth factor. Targeting VEGF in cardiovascular disease has been challenging, while modulation of the Angpt system seems more promising. CONCLUSION: Future studies will establish the translatability of therapies targeting vascular growth factors for heart and kidney disease in patients with diabetes.

Health technology assessment through Six Sigma Methodology to assess cemented and uncemented protheses in total hip arthroplasty.

The purpose of this study is to use Health Technology Assessment (HTA) through the Six Sigma (SS) and DMAIC (Define, Measure, Analyse, Improve, Control) problem-solving strategies for comparing cemented and uncemented prostheses in terms of the costs incurred for Total hip arthroplasty (THA) and the length of hospital stay (LOS). Multinomial logistic regression analysis for modelling the data was also performed. Quantitative parameters extracted from gait analysis, electromyography and computed tomography images were used to compare the approaches, but the analysis did not show statistical significance. The variables regarding costs were studied with the Mann-Whitney and Kruskal-Wallis tests. No statistically significant difference between cemented and uncemented prosthesis for the total cost of LOS was found, but the cost of the surgeon had an influence on the overall expenses, affecting the cemented prosthetic approach. The material costs of surgery for the uncemented prosthesis and the cost of theatre of surgery for the cemented prosthesis were the most influential. Multinomial logistic regression identified the Vastus Lateralis variable as statistically significant. The overall accuracy of the model is 93.0%. The use of SS and DMAIC cycle as tools of HTA proved that the cemented and uncemented approaches for THA have similar costs and LOSy.

Head-to-head comparison of diagnostic accuracy of stress-only myocardial perfusion imaging with conventional and cadmium-zinc telluride single-photon emission computed tomography in women with suspected coronary artery disease.

BACKGROUND: Breast attenuation may impact the diagnostic accuracy of stress myocardial perfusion imaging (MPI) with single-photon emission computed tomography (SPECT). We compared the performance of conventional (C)-SPECT and cadmium-zinc-telluride (CZT)-SPECT systems in women with low-intermediate likelihood of coronary artery disease (CAD). METHODS AND RESULTS: A total of 109 consecutive women underwent stress-optional rest MPI by both C-SPECT and CZT-SPECT. In the overall study population, a weak albeit significant correlation between total perfusion defect (TPD) measured by C-SPECT and CZT-SPECT was observed (r = 0.38, P < .001) and at Bland-Altman analysis the mean difference in TPD (C-SPECT minus CZT-SPECT) was 2.40% (P < .001). Overall concordance of semi-quantitative diagnostic performance between C-SPECT and CZT-SPECT was observed in 52 (48%) women with a κ value of 0.09. Normalcy rate was significantly higher using CZT-SPECT compared to C-SPECT (P < .001). Machine learning analysis performed through the implementation of J48 algorithm proved that CZT-SPECT has higher sensitivity, specificity, and accuracy than C-SPECT. CONCLUSIONS: In women with low-intermediate likelihood of CAD, there is a poor concordance of diagnostic performance between C-SPECT and CZT-SPECT, and CZT-SPECT allows better normalcy rate detection compared to C-SPECT.

Healthy Aging Within an Image: Using Muscle Radiodensitometry and Lifestyle Factors to Predict Diabetes and Hypertension.

The strong age dependency of many deleterious health outcomes likely reflects the cumulative effects from a variety of risk and protective factors that occur over one's life course. This notion has become increasingly explored in the etiology of chronic disease and associated comorbidities in aging. Our recent work has shown the robust classification of individuals at risk for cardiovascular pathophysiology using CT-based soft tissue radiodensity parameters obtained from nonlinear trimodal regression analysis (NTRA). Past and present lifestyle influences the incidence of comorbidities like hypertension (HTN), diabetes (DM) and cardiac diseases. 2,943 elderly subjects from the AGES-Reykjavik study were sorted into a three-level binary-tree structure defined by: 1) lifestyle factors (smoking and self-reported physical activity level), 2) comorbid HTN or DM, and 3) cardiac pathophysiology. NTRA parameters were extracted from mid-thigh CT cross-sections to quantify radiodensitometric changes in three tissue types: lean muscle, fat, and loose-connective tissue. Between-group differences were assessed at each binary-tree level, which were then used in tree-based machine learning (ML) models to classify subjects with DM or HTN. Classification scores for detecting HTN or DM based on lifestyle factors were excellent (AUCROC: 0.978 and 0.990, respectively). Finally, tissue importance analysis underlined the comparatively-high significance of connective tissue parameters in ML classification, while predictive models of DM onset from five-year longitudinal data gave a classification accuracy of 94.9%. Altogether, this work serves as an important milestone toward the construction of predictive tools for assessing the impact of lifestyle factors and healthy aging based on a single image.

The endoplasmic reticulum stress and the unfolded protein response in kidney disease: Implications for vascular growth factors.

Acute kidney injury (AKI) and chronic kidney disease (CKD) represent an important challenge for healthcare providers. The identification of new biomarkers/pharmacological targets for kidney disease is required for the development of more effective therapies. Several studies have shown the importance of the endoplasmic reticulum (ER) stress in the pathophysiology of AKI and CKD. ER is a cellular organelle devolved to protein biosynthesis and maturation, and cellular detoxification processes which are activated in response to an insult. This review aimed to dissect the cellular response to ER stress which manifests with activation of the unfolded protein response (UPR) with its major branches, namely PERK, IRE1α, ATF6 and the interplay between ER and mitochondria in the pathophysiology of kidney disease. Further, we will discuss the relationship between mediators of renal injury (with specific focus on vascular growth factors) and ER stress and UPR in the pathophysiology of both AKI and CKD with the aim to propose potential new targets for treatment for kidney disease.

MRI Radiomics for the Prediction of Fuhrman Grade in Clear Cell Renal Cell Carcinoma: a Machine Learning Exploratory Study.

The Fuhrman nuclear grade is a recognized prognostic factor for patients with clear cell renal cell carcinoma (CCRCC) and its pre-treatment evaluation significantly affects decision-making in terms of management. In this study, we aimed to assess the feasibility of a combined approach of radiomics and machine learning based on MR images for a non-invasive prediction of Fuhrman grade, specifically differentiation of high- from low-grade tumor and grade assessment. Images acquired on a 3-Tesla scanner (T2-weighted and post-contrast) from 32 patients (20 with low-grade and 12 with high-grade tumor) were annotated to generate volumes of interest enclosing CCRCC lesions. After image resampling, normalization, and filtering, 2438 features were extracted. A two-step feature reduction process was used to between 1 and 7 features depending on the algorithm employed. A J48 decision tree alone and in combination with ensemble learning methods were used. In the differentiation between high- and low-grade tumors, all the ensemble methods achieved an accuracy greater than 90%. On the other end, the best results in terms of accuracy (84.4%) in the assessment of tumor grade were achieved by the random forest. These evidences support the hypothesis that a combined radiomic and machine learning approach based on MR images could represent a feasible tool for the prediction of Fuhrman grade in patients affected by CCRCC.

Prediction of Tumor Grade and Nodal Status in Oropharyngeal and Oral Cavity Squamous-cell Carcinoma Using a Radiomic Approach.

BACKGROUND/AIM: To investigate whether a radiomic machine learning (ML) approach employing texture-analysis (TA) features extracted from primary tumor lesions (PTLs) is able to predict tumor grade (TG) and nodal status (NS) in patients with oropharyngeal (OP) and oral cavity (OC) squamous-cell carcinoma (SCC). PATIENTS AND METHODS: Contrast-enhanced CT images of 40 patients with OP and OC SCC were post-processed to extract TA features from PTLs. A feature selection method and different ML algorithms were applied to find the most accurate subset of features to predict TG and NS. RESULTS: For the prediction of TG, the best accuracy (92.9%) was achieved by Naïve Bayes (NB), bagging of NB and K Nearest Neighbor (KNN). For the prediction of NS, J48, NB, bagging of NB and boosting of J48 overcame the accuracy of 90%. CONCLUSION: A radiomic ML approach applied to PTLs is able to predict TG and NS in patients with OC and OP SCC.

Overexpression of Circulating Soluble Nogo-B Improves Diabetic Kidney Disease by Protecting the Vasculature.

Damage to the vasculature is the primary mechanism driving chronic diabetic microvascular complications such as diabetic nephropathy, which manifests as albuminuria. Therefore, treatments that protect the diabetic vasculature have significant therapeutic potential. Soluble neurite outgrowth inhibitor-B (sNogo-B) is a circulating N-terminus isoform of full-length Nogo-B, which plays a key role in vascular remodeling following injury. However, there is currently no information on the role of sNogo-B in the context of diabetic nephropathy. We demonstrate that overexpression of sNogo-B in the circulation ameliorates diabetic kidney disease by reducing albuminuria, hyperfiltration, and abnormal angiogenesis and protecting glomerular capillary structure. Systemic sNogo-B overexpression in diabetic mice also associates with dampening vascular endothelial growth factor-A signaling and reducing endothelial nitric oxide synthase, AKT, and GSK3ß phosphorylation. Furthermore, sNogo-B prevented the impairment of tube formation, which occurred when human endothelial cells were exposed to sera from patients with diabetic kidney disease. Collectively, these studies provide the first evidence that sNogo-B protects the vasculature in diabetes and may represent a novel therapeutic target for diabetic vascular complications.

Nanomechanical DNA resonators for sensing and structural analysis of DNA-ligand complexes.

The effect of direct or indirect binding of intercalant molecules on DNA structure is of fundamental importance in understanding the biological functioning of DNA. Here we report on self-suspended DNA nanobundles as ultrasensitive nanomechanical resonators for structural studies of DNA-ligand complexes. Such vibrating nanostructures represent the smallest mechanical resonator entirely composed of DNA. A correlative analysis between the mechanical and structural properties is exploited to study the intrinsic changes of double strand DNA, when interacting with different intercalant molecules (YOYO-1 and GelRed) and a chemotherapeutic drug (Cisplatin), at different concentrations. Possible implications of our findings are related to the study of interaction mechanism of a wide category of molecules with DNA, and to further applications in medicine, such as optimal titration of chemotherapeutic drugs and environmental studies for the detection of heavy metals in human serum.

A multi-level memristor based on atomic layer deposition of iron oxide.

This work reports the fabrication of memristive devices based on iron oxide (Fe2O3) thin films grown by atomic layer deposition (ALD) using ferrocene as iron precursor and ozone as oxidant. An excellent control of the ALD process was achieved by using an experimental procedure based on a sequence of micro-pulses, which provided long residence time and homogeneous diffusion of precursors, allowing ALD of thin films with smooth morphology and crystallinity which was found to increase with layer thickness, at temperatures as low as 250 °C. The resistive switching of symmetric Pt/Fe2O3/Pt thin film devices exhibited bipolar mode with good stability and endurance. Multi-level switching was achieved via current and voltage control. It was proved that the ON state regime can be tuned by changing the current compliance while the OFF state can be changed to intermediate levels by decreasing the maximum voltage during RESET. The structural analysis of the switched oxide layer revealed the presence of nano-sized crystalline domains corresponding to different iron oxide phases, suggesting that Joule heating effects during I-V cycling are responsible for a crystallization process of the pristine amorphous layer.

Renal biopsy: Still a landmark for the nephrologist.

Renal biopsy was performed for the first time more than one century ago, but its clinical use was routinely introduced in the 1950s. It is still an essential tool for diagnosis and choice of treatment of several primary or secondary kidney diseases. Moreover, it may help to know the expected time of end stage renal disease. The indications are represented by nephritic and/or nephrotic syndrome and rapidly progressive acute renal failure of unknown origin. Nowadays, it is performed mainly by nephrologists and radiologists using a 14-18 gauges needle with automated spring-loaded biopsy device, under real-time ultrasound guidance. Bleeding is the major primary complication that in rare cases may lead to retroperitoneal haemorrhage and need for surgical intervention and/or death. For this reason, careful evaluation of risks and benefits must be taken into account, and all procedures to minimize the risk of complications must be observed. After biopsy, an observation time of 12-24 h is necessary, whilst a prolonged observation may be needed rarely. In some cases it could be safer to use different techniques to reduce the risk of complications, such as laparoscopic or transjugular renal biopsy in patients with coagulopathy or alternative approaches in obese patients. Despite progress in medicine over the years with the introduction of more advanced molecular biology techniques, renal biopsy is still an irreplaceable tool for nephrologists.