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Head and neck cancer represents the seventh most common neoplasm worldwide, with squamous cell carcinoma being the most represented histologic variant. The rising incidence of the neoplastic pathology of this district, coupled with the drastic changes in its epidemiology over the past decades, have posed significant challenges to physicians worldwide in terms of diagnosis, prognosis, and treatment. In order to meet these challenges, a considerable amount of effort has been spent by the authors of the recent literature to explore new technologies and their possible employment for the better diagnostic and prognostic definition of head and neck squamous cell carcinoma (HNSCC). Among these technologies, a growing interest has been gathering around the possible applications of dual-energy computed tomography (DECT) in head and neck pathology. Dual-energy computed tomography (DECT) utilizes two distinct X-ray energy spectra to obtain two datasets in a single scan, allowing for material differentiation based on unique attenuation profiles. DECT offers key benefits such as enhanced contrast resolution, reduced beam-hardening artifacts, and precise iodine quantification through monochromatic reconstructions. It also creates material decomposition images, like iodine maps, aiding in tumor characterization and therapy assessment. This paper aims to summarize recent findings on the use of DECT in HNSCC, providing a comprehensive overview to aid further research and exploration in the field.

Goal: Organ perfusion is a vast subject with several techniques, the most common being Hypothermic Machine Perfusion (HMP) and Normothermic Machine Perfusion (NMP). In this paper, a new machine developed at the Biolab of the University of Florence has been tested to validate its capability to perform a well-controlled Oxygenated Rewarming (COR) phase and maintain stability during the NMP phase. Methods: The tests (n = 5) were conducted on fresh porcine livers and evaluated according to liver perfusion standards. The developed machine is based on a previous version, with an integrated control and sensor system and a complete mechanical and electronic redesign. Results: The results demonstrate the excellent usability of the machine and its ability to effectively maintain the organs in good condition. The new system performed well, and the measures made on the livers were satisfactory for good preservation of the organ. Conclusion: This study showed the effectiveness of the developed machine. Future development of the system will include a more sophisticated control system to ensure the correct parameters for perfusion.

Background: COVID-19 has put a huge strain on the healthcare systems worldwide, requiring unprecedented intensive care resources. There is still an unmet clinical need for easily available biomarkers capable of predicting the risk for severe disease. The main goal of this prospective multicenter study was to identify biomarkers that could predict ICU admission and in-hospital mortality. Methods: We prospectively recruited COVID-19 PCR positive patients in two hospitals, in Belgium and Italy. Blood samples were collected at hospital admission and 20 potential biomarkers were measured with the Luminex technology. Logistic regression models were performed to identify the biomarkers that, alone or together, were associated with patient disease severity. Results: Our study demonstrates that elevated levels of circulating inflammatory cytokines were associated with disease severity in COVID-19 hospitalized patients. CXCL10, IL-4, IL-6 and MCP-1 values were predictive of ICU admission. Elevated levels of IL-6 and MCP-1 were also associated with in hospital death in COVID-19 hospitalized patients. Conclusion: Altogether, elevated and correlated inflammatory cytokines in the blood of COVID-19 patients at hospital admission are predictive of disease severity and suggest a dysregulated inflammation induced by SARS-CoV-2 infection.

Abstract Background COVID-19 is a pandemic disease affecting predominantly the respiratory apparatus with clinical manifestations ranging from asymptomatic to respiratory failure. Chest CT is a crucial tool in diagnosing and evaluating the severity of pulmonary involvement through dedicated scoring systems. Nonetheless, many questions regarding the relationship of radiologic and clinical features of the disease have emerged in multidisciplinary meetings. The aim of this retrospective study was to explore such relationship throughout an innovative and alternative approach. Materials and methods This study included 550 patients (range 25–98 years; 354 males, mean age 66.1; 196 females, mean age 70.9) hospitalized for COVID-19 with available radiological and clinical data between 1 March 2021 and 30 April 2022. Radiological data included CO-RADS, chest CT score, dominant pattern, and typical/atypical findings detected on CT examinations. Clinical data included clinical score and outcome. The relationship between such features was investigated through the development of the main four frequently asked questions summarizing the many issues arisen in multidisciplinary meetings, as follows 1) CO-RADS, chest CT score, clinical score, and outcomes; 2) the involvement of a specific lung lobe and outcomes; 3) dominant pattern/distribution and severity score for the same chest CT score; 4) additional factors and outcomes. Results 1) If CT was suggestive for COVID, a strong correlation between CT/clinical score and prognosis was found; 2) Middle lobe CT involvement was an unfavorable prognostic criterion; 3) If CT score 50%, crazy paving as dominant pattern leaded to a 15% increased death rate, stacked up against other patterns, thus almost doubling it; 4) Additional factors usually did not matter, but lymph-nodes and pleural effusion worsened prognosis. Conclusions This study outlined those radiological features of COVID-19 most relevant towards disease severity and outcome with an innovative approach.

Diagnosing COVID-19 and treating its complications remains a challenge. This review reflects the perspective of some of the Dragon (IMI 2-call 21, #101005122) research consortium collaborators on the utility of bronchoalveolar lavage (BAL) in COVID-19. BAL has been proposed as a potentially useful diagnostic tool to increase COVID-19 diagnosis sensitivity. In both critically ill and non-critically ill COVID-19 patients, BAL has a relevant role in detecting other infections or supporting alternative diagnoses and can change management decisions in up to two-thirds of patients. BAL is used to guide steroid and immunosuppressive treatment and to narrow or discontinue antibiotic treatment, reducing the use of unnecessary broad antibiotics. Moreover, cellular analysis and novel multi-omics techniques on BAL are of critical importance for understanding the microenvironment and interaction between epithelial cells and immunity, revealing novel potential prognostic and therapeutic targets. The BAL technique has been described as safe for both patients and healthcare workers in more than a thousand procedures reported to date in the literature. Based on these preliminary studies, we recognize that BAL is a feasible procedure in COVID-19 known or suspected cases, useful to properly guide patient management, and has great potential for research.

Abstract Background The role of computed tomography (CT) in the diagnosis and characterization of coronavirus disease 2019 (COVID-19) pneumonia has been widely recognized. We evaluated the performance of a software for quantitative analysis of chest CT, the LungQuant system, by comparing its results with independent visual evaluations by a group of 14 clinical experts. The aim of this work is to evaluate the ability of the automated tool to extract quantitative information from lung CT, relevant for the design of a diagnosis support model. Methods LungQuant segments both the lungs and lesions associated with COVID-19 pneumonia (ground-glass opacities and consolidations) and computes derived quantities corresponding to qualitative characteristics used to clinically assess COVID-19 lesions. The comparison was carried out on 120 publicly available CT scans of patients affected by COVID-19 pneumonia. Scans were scored for four qualitative metrics: percentage of lung involvement, type of lesion, and two disease distribution scores. We evaluated the agreement between the LungQuant output and the visual assessments through receiver operating characteristics area under the curve (AUC) analysis and by fitting a nonlinear regression model. Results Despite the rather large heterogeneity in the qualitative labels assigned by the clinical experts for each metric, we found good agreement on the metrics compared to the LungQuant output. The AUC values obtained for the four qualitative metrics were 0.98, 0.85, 0.90, and 0.81. Conclusions Visual clinical evaluation could be complemented and supported by computer-aided quantification, whose values match the average evaluation of several independent clinical experts. Key points We conducted a multicenter evaluation of the deep learning-based LungQuant automated software. We translated qualitative assessments into quantifiable metrics to characterize coronavirus disease 2019 (COVID-19) pneumonia lesions. Comparing the software output to the clinical evaluations, results were satisfactory despite heterogeneity of the clinical evaluations. An automatic quantification tool may contribute to improve the clinical workflow of COVID-19 pneumonia.

The aim of this work is to identify MRI texture features able to predict the response to radio-chemotherapy (RT-CHT) in patients with naso-oropharyngeal carcinoma (NPC-OPC) before treatment in order to help clinical decision making. Textural features were derived from ADC maps and post-gadolinium T1-images on a single MRI machine for 37 patients with NPC-OPC. Patients were divided into two groups (responders/non-responders) according to results from MRI scans and 18F-FDG-PET/CT performed at follow-up 3–4 and 12 months after therapy and biopsy. Pre-RT-CHT lesions were segmented, and radiomic features were extracted. A non-parametric Mann–Whitney test was performed. A p-value < 0.05 was considered significant. Receiver operating characteristic curves and area-under-the-curve values were generated; a 95% confidence interval (CI) was reported. A radiomic model was constructed using the LASSO algorithm. After feature selection on MRI T1 post-contrast sequences, six features were statistically significant: gldm_DependenceEntropy and DependenceNonUniformity, glrlm_RunEntropy and RunLengthNonUniformity, and glszm_SizeZoneNonUniformity and ZoneEntropy, with significant cut-off values between responder and non-responder group. With the LASSO algorithm, the radiomic model showed an AUC of 0.89 and 95% CI: 0.78–0.99. In ADC, five features were selected with an AUC of 0.84 and 95% CI: 0.68–1. Texture analysis on post-gadolinium T1-images and ADC maps could potentially predict response to therapy in patients with NPC-OPC who will undergo exclusive treatment with RT-CHT, being, therefore, a useful tool in therapeutical–clinical decision making.

Artificial intelligence (AI) can be considered the real revolution of the 21st century [...]

Introduction: Sinonasal intestinal-type adenocarcinomas (ITACs) are rare and aggressive tumors, closely related to professional exposure to wood dusts or leather. Here we explored the role of non-coding RNAs controlling MUC2 in liquid biopsies and tumors from ITAC patients with the aim of identifying biomarkers and molecular mechanisms to improve early diagnosis, prognosis, and therapeutic approaches for this rare cancer. Methods: MiR-34c-3p, lncRNA AF147447 and MUC2 were measured in tumors and normal mucosa, in nasal washings (NW) from the affected and non-affected nostril and in plasma from 17 ITAC patients. The Apparent Diffusion Coefficient (ADC) was also evaluated by Magnetic Resonance Imaging. Results: MiR-34c was higher in ITACs compared to the corresponding normal mucosa (p = 0.021). Differentiated tumors exhibited higher miR-34c levels (p = 0.025) and lower ADC values (p

Abstract Mandibular fractures are among the most common maxillofacial fractures observed in emergency rooms and are mainly caused by road accidents. The clinical features of mandibular fractures include malocclusion and loss of mandibular function. Panoramic radiography is usually limited to isolated lesions, whereas computed tomography is the tool of choice for all other facial traumatic events. No reference standard classification system for the different types of mandibular fractures is defined. Therapeutic options include a conservative approach or surgical treatment based on the anatomic area and the severity of fracture. The main purpose of this pictorial review is to illustrate a practical description of the pathophysiology of mandibular fractures and describe both the imaging techniques to recognise them and the therapeutic indications.

ObjectiveAlthough interstitial lung disease (ILD) is a major cause of morbidity and mortality in systemic sclerosis (SSc), its prognostication remains challenging. Given that CT represents the gold standard imaging technique in ILD assessment, a systematic review on chest CT findings as predictors of mortality or ILD progression in SSc-ILD was performed.Materials and MethodsThree databases (Medline, Embase, and Web of Science) were searched to identify all studies analyzing CT mortality or ILD progression predictors in SSc-ILD, from inception to December 2020. ILD progression was defined by worsening of forced vital capacity and/or CT ILD findings. Manuscripts not written in English, with not available full-text, not focusing on SSc-ILD or with SSc-ILD not extrapolated, otherwise with overlap syndromes, pediatric patients,

The purpose of this study was to differentiate post-chemoradiotherapy (CRT) changes from tumor persistence/recurrence in early follow-up of naso-oropharyngeal carcinoma on magnetic resonance (MRI) with diffusion (DWI) and dynamic contrast-enhanced perfusion-weighted imaging (DCE-PWI). A total of 37 patients were assessed with MRI both for tumor staging and 4-month follow-up from ending CRT. Mean apparent diffusion coefficient (ADC) values, area under the curve (AUC), and K(trans) values were calculated from DWI and DCE-PWI images, respectively. DWI and DCE-PWI values of primary tumor (ADC, AUC, K(trans)pre), post-CRT changes (ADC, AUC, K(trans)post), and trapezius muscle as a normative reference before and after CRT (ADC, AUC, K(trans)muscle pre and muscle post; AUCpost/muscle post:AUCpre/muscle pre (AUCpost/pre/muscle); K(trans)post/muscle post:K(trans)pre/muscle pre (K(trans)post/pre/muscle) were assessed. In detecting post-CRT changes, ADCpost > 1.33 × 10−3 mm2/s and an increase >0.72 × 10−3 mm2/s and/or >65.5% between ADCpost and ADCpre values (ADCpost-pre; ADCpost-pre%) had 100% specificity, whereas hypointense signal intensity on DWIb800 images showed specificity 80%. Although mean AUCpost/pre/muscle and K(trans)post/pre/muscle were similar both in post-CRT changes (1.10 ± 0.58; 1.08 ± 0.91) and tumor persistence/recurrence (1.09 ± 0.11; 1.03 ± 0.12), K(trans)post/pre/muscle values < 0.85 and >1.20 suggested post-CRT fibrosis and inflammatory edema, respectively. In early follow-up of naso-oropharyngeal carcinoma, our sample showed that ADCpost > 1.33 × 10−3 mm2/s, ADCpost-pre% > 65.5%, and ADCpost-pre > 0.72 × 10−3 mm2/s identified post-CRT changes with 100% specificity. K(trans)post/pre/muscle values less than 0.85 suggested post-CRT fibrosis, whereas K(trans)post/pre/muscle values more than 1.20 indicated inflammatory edema.

Deep learning has achieved remarkable progress, particularly in neuroimaging analysis. Deep learning applications have also been extended from adult to pediatric medical images, and thus, this paper aims to present a systematic review of this recent research. We first introduce the commonly used deep learning methods and architectures in neuroimaging, such as convolutional neural networks, auto-encoders, and generative adversarial networks. A non-exhaustive list of commonly used publicly available pediatric neuroimaging datasets and repositories are included, followed by a categorical review of recent works in pediatric MRI-based deep learning studies in the past five years. These works are categorized into recognizing neurodevelopmental disorders, identifying brain and tissue structures, estimating brain age/maturity, predicting neurodevelopment outcomes, and optimizing MRI brain imaging and analysis. Finally, we also discuss the recent achievements and challenges on these applications of deep learning to pediatric neuroimaging.

Arterial stenosis is one of the main vascular diseases that are treated with minimally invasive surgery approaches. The aim of this study was to provide a tool to support the medical doctor in planning endovascular surgery, allowing the rapid detection of stenotic vessels and the quantification of the stenosis. Skeletonization was used to improve vessels’ visualization. The distance transform was used to obtain a linear representation of the diameter of critical vessels selected by the user. The system also provides an estimate of the exact distance between landmarks on the vascular tree and the occlusion, important information that can be used in the planning of the surgery. The advantage of the proposed tool is to lead the examination on the linear representation of the chosen vessels that are free from tortuous vascular courses and from vessel crossings.

The aim of this single-center, observational, retrospective study was to investigate magnetic resonance imaging (MRI) biomarkers for the assessment of radiotherapy (RT)-induced xerostomia. Twenty-seven patients who underwent radiation therapy for oropharyngeal cancer were divided into three groups according to the severity of their xerostomia—mild, moderate, and severe—clinically confirmed with the Common Terminology Criteria for Adverse Events (CTCAE). No severe xerostomia was found. Conventional and functional MRI (perfusion- and diffusion- weighted imaging) performed both pre- and post-RT were studied for signal intensity, mean apparent diffusion coefficient (ADC) values, k-trans, and area under the perfusion curves. Contrast-enhanced T1 images and ADC maps were imported into 3D slicer software, and salivary gland volumes were segmented. A total of 107 texture features were derived. T-Student and Wilcoxon signed-rank tests were performed on functional MRI parameters and texture analysis features to identify the differences between pre- and post-RT populations. A p-value < 0.01 was defined as acceptable. Receiver operating characteristic (ROC) curves were plotted for significant parameters to discriminate the severity of xerostomia in the pre-RT population. Conventional and functional MRI did not yield statistically significant results; on the contrary, five texture features showed significant variation between pre- and post-RT on the ADC maps, of which only informational measure of correlation 1 (IMC 1) was able to discriminate the severity of RT-induced xerostomia in the pre-RT population (area under the curve (AUC) > 0.7). Values lower than the cut-off of −1.473 × 10−11 were associated with moderate xerostomia, enabling the differentiation of mild xerostomia from moderate xerostomia with a 73% sensitivity, 75% specificity, and 75% diagnostic accuracy. Therefore, the texture feature IMC 1 on the ADC maps allowed the distinction between different degrees of severity of RT-induced xerostomia in the pre-RT population. Accordingly, texture analysis on ADC maps should be considered a useful tool to evaluate salivary gland radiosensitivity and help identify patients at risk of developing more serious xerostomia before radiation therapy is administered.

Abstract Background The mechanisms underlying airflow obstruction in COPD cannot be distinguished by standard spirometry. We ascertain whether mathematical modeling of airway biomechanical properties, as assessed from spirometry, could provide estimates of emphysema presence and severity, as quantified by computed tomography (CT) metrics and CT-based radiomics. Methods We quantified presence and severity of emphysema by standard CT metrics (VIDA) and co-registration analysis (ImbioLDA) of inspiratory-expiratory CT in 194 COPD patients who underwent pulmonary function testing. According to percentages of low attenuation area below − 950 Hounsfield Units (%LAA-950insp) patients were classified as having no emphysema (NE) with %LAA-950insp

Augmented reality (AR) is an innovative system that enhances the real world by superimposing virtual objects on reality. The aim of this study was to analyze the application of AR in medicine and which of its technical solutions are the most used. We carried out a scoping review of the articles published between 2019 and February 2022. The initial search yielded a total of 2649 articles. After applying filters, removing duplicates and screening, we included 34 articles in our analysis. The analysis of the articles highlighted that AR has been traditionally and mainly used in orthopedics in addition to maxillofacial surgery and oncology. Regarding the display application in AR, the Microsoft HoloLens Optical Viewer is the most used method. Moreover, for the tracking and registration phases, the marker-based method with a rigid registration remains the most used system. Overall, the results of this study suggested that AR is an innovative technology with numerous advantages, finding applications in several new surgery domains. Considering the available data, it is not possible to clearly identify all the fields of application and the best technologies regarding AR.

The aim of the study was to evaluate the changes in skeletal, dentoalveolar, and airway dimensions induced by the mandibular anterior repositioning appliance (MARA) in the treatment of Class II malocclusion. A group of 24 Class II patients treated with MARA was evaluated with lateral cephalograms at the start (13.8 ± 2 years) and at the end of treatment (15.7 ± 1.9 years) and compared with a matched untreated Class II group of 20 subjects selected from the archive of the AOOF Legacy. Statistical comparisons were performed with the Student’s t-test. No significant differences were observed for the sagittal airway dimensions. The MARA group showed a significant improvement of Wits (−1.4 mm; p = 0.009) and ANB angle (−0.6°; p = 0.038), a reduction in the overjet (−1.9 mm; p = 0.001), of the overbite (−1.5 mm; p = 0.005), an improvement of molar relationship (1.8 mm; p = 0.000) and a proclination of the lower incisors (2.7°; p = 0.012). During active treatment, MARA did not produce significant favorable changes in the sagittal airway dimensions in Class II subjects when compared with untreated controls. Clinically, these findings highlighted that MARA was able to provide an effective correction of Class II malocclusion with favorable dentoskeletal changes.

The oral cavity constitutes a complex anatomical area that can be affected by many developmental, inflammatory, and tumoural diseases. MultiSlice Computed Tomography (MSCT) and Magnetic Resonance Imaging (MRI) currently represent the essential and complementary imaging techniques for detecting oral cavity abnormalities. Advanced MRI with diffusion-weighted imaging (DWI) and dynamic contrast-enhanced perfusion-weighted imaging (DCE-PWI) has recently increased the ability to characterise oral lesions and distinguish disease recurrences from post therapy changes. The analysis of the oral cavity area via imaging techniques is also complicated both by mutual close appositions of different mucosal surfaces and metal artifacts from dental materials. Nevertheless, an exact identification of oral lesions is made possible thanks to dynamic manoeuvres and specific stratagems applicable on MSCT and MRI acquisitions. This study summarises the currently available imaging techniques for oral diseases, with particular attention to the role of DWI, DCE-PWI, and dynamic manoeuvres. We also propose MSCT and MRI acquisition protocols for an accurate study of the oral cavity area.

Abstract The latest (4th) edition of the World Health Organization (WHO) Classification of Head and Neck Tumours, published in January 2017, has reclassified keratocystic odontogenic tumour as odontogenic keratocyst. Therefore, odontogenic keratocysts (OKCs) are now considered benign cysts of odontogenic origin that account for about 10% of all odontogenic cysts. OKCs arise from the dental lamina and are characterised by a cystic space containing desquamated keratin with a uniform lining of parakeratinised squamous epithelium. The reported age distribution of OKCs is considerably wide, with a peak of incidence in the third decade of life and a slight male predominance. OKCs originate in tooth-bearing regions and the mandible is more often affected than the maxilla. In the mandible, the most common location is the posterior sextant, the angle or the ramus. Conversely, the anterior sextant and the third molar region are the most common sites of origin in the maxilla. OKCs are characterised by an aggressive behaviour with a relatively high recurrence rate, particularly when OKCs are associated with syndromes. Multiple OKCs are typically associated with the nevoid basal cell carcinoma syndrome (NBCCS), an autosomal dominant multisystemic disease. Radiological imaging, mainly computed tomography (CT) and, in selected cases, magnetic resonance imaging (MRI), plays an important role in the diagnosis and management of OKCs. Therefore, the main purpose of this pictorial review is to present the imaging appearance of OKCs underlining the specific findings of different imaging modalities and to provide key radiologic features helping the differential diagnoses from other cystic and neoplastic lesions of odontogenic origin. Key Points • Panoramic radiography is helpful in the preliminary assessment of OKCs. • CT is considered the tool of choice in the evaluation of OKCs. • MRI with DWI or DKI can help differentiate OKCs from other odontogenic lesions. • Ameloblastoma, dentigerous and radicular cysts should be considered in the differential diagnosis. • The presence of multiple OKCs is one of the major criteria for the diagnosis of NBCCS.

The aim of this study was to compare the efficacy of early treatment of Class III malocclusions with rapid maxillary expansion (RME) and facial mask (FM) versus the removable mandibular retractor (RMR) re-evaluated at a postpubertal observation on lateral cephalograms. All prepubertal patients with Class III malocclusion treated consecutively from 1986 to 2013 by means of RME/FM or RMR were analyzed. Twenty-nine patients treated with RME/FM therapy and 23 patients treated with RMR were selected. Lateral cephalograms were available at 3 time points, before treatment (T1), at the end of active treatment (T2), and at a postpubertal observation (T3). Statistical comparisons were performed with independent sample t tests or Mann–Whitney tests. During the T1–T3 interval, a significantly greater maxillary protraction (SNA +1.5 mm, p = 0.031) and significantly greater improvements in ANB and Wits appraisal (+1.9 degrees, p = 0.002, and +2.2 mm, p = 0.012, respectively) were recorded in the RME/FM group. No statistically significant changes could be found in vertical skeletal measurements. In the dentoalveolar region, the RME/FM group showed a significantly greater correction of the molar relationship (−1.5 mm, p = 0.021). Early treatment of Class III malocclusion with RME/FM protocol in comparison with RMR protocol showed a greater maxillary advancement and greater improvements in sagittal skeletal Class III relationships.

Objectives: A panoramic radiograph (PAN) is the most frequently diagnostic imaging technique carried out in dentistry and oral surgery. The correct performance of image acquisition is crucial to obtain adequate image quality. The aim of the present study is to (i) review the principles of PAN image acquisition and (ii) describe positioning errors and artefacts that may affect PAN image quality. Methods: Articles regarding PAN acquisition principles, patient’s positioning errors, artefacts, and image quality were retrieved from the literature. Results: Head orientation is of the utmost importance in guaranteeing correct image acquisition. Symmetry, occlusal plane inclination, mandibular condyles localization, cervical spine position, aspect of upper teeth root apexes, exposure parameters, and metal and motion artefacts are factors that greatly affect the image quality of a successful PAN. Conclusions: Several factors are the basis for PAN performance; therefore, a systematic approach that takes into account correct patient positioning and preparation is strongly suggested to improve overall examination quality.

Aim: To evaluate the diagnostic accuracy of panoramic radiography (PAN) for the identification of infectious foci of the tooth and periradicular bone before definitive chemo-radiotherapy treatment for oropharyngeal cancer, using multislice spiral computed tomography (MSCT) imaging as the reference standard. Materials and methods: 50 patients with oropharyngeal cancer who had performed both pre-treatment MSCT and PAN were retrospectively evaluated. Pre-radiotherapy MSCT showed 65 deep caries, 37 root remnants, 143 stage III periodontal diseases, and 77 apical periodontitis, for a total of 322 infectious foci. The same number of healthy teeth (control group) was selected via MSCT to be analysed by PAN. Sensitivity, specificity, positive and negative predictive values, and diagnostic accuracy for PAN images with respect to MSCT imaging were examined. Results: PAN showed sensitivity, negative predictive value, and diagnostic accuracy of 100% for deep caries, root remnants, and stage III periodontal disease, whereas there were 46.8%, 64.7%, and 72.1% apical periodontitis respectively. Conclusions: PAN did not show great diagnostic accuracy in the assessment of apical periodontitis, and therefore maxillofacial MSCT carried out before chemo-radiotherapy treatment should always be examined to identify dental and jaw diseases. Deep caries, root remnants, and stage III periodontal disease were perfectly detected on PAN.

Kidneys of mice, rats and humans possess progenitors that maintain daily homeostasis and take part in endogenous regenerative processes following injury, owing to their capacity to proliferate and differentiate. In the glomerular and tubular compartments of the nephron, consistent studies demonstrated that well-characterized, distinct populations of progenitor cells, localized in the parietal epithelium of Bowman capsule and scattered in the proximal and distal tubules, could generate segment-specific cells in physiological conditions and following tissue injury. However, defective or abnormal regenerative responses of these progenitors can contribute to pathologic conditions. The molecular characteristics of renal progenitors have been extensively studied, revealing that numerous classical and evolutionarily conserved pathways, such as Notch or Wnt/β-catenin, play a major role in cell regulation. Others, such as retinoic acid, renin-angiotensin-aldosterone system, TLR2 (Toll-like receptor 2) and leptin, are also important in this process. In this review, we summarize the plethora of molecular mechanisms directing renal progenitor responses during homeostasis and following kidney injury. Finally, we will explore how single-cell RNA sequencing could bring the characterization of renal progenitors to the next level, while knowing their molecular signature is gaining relevance in the clinic.