Correlation between parodontal indexes and orthodontic retainers: prospective study in a group of 16 patients

Purpose. Fixed retainers are used to stabilize dental elements after orthodontic treatment. Being it a permanent treatment, it is necessary to instruct patients about a constant and continuous monitoring of their periodontal conditions and a correct oral hygiene. The aim of this study was to highlight the possible adverse effects of bonded retainers on parameters correlated to the health conditions of periodontal tissues. Materials and methods. We selected 16 patients, under treatment in the Orthodontics Department of University of Bari Dental School, who had undergone a lingual retainer insertion at the end of the orthodontic treatment. The patients were then divided into two groups (Control Group and Study Group) and monitored for 3 and 36 months, respectively. The following indexes were taken into consideration: gingival index (GI), plaque index (PI) and the presence of calculus (Calculus Index, CI), the probing depth and the presence of gingival recession on the six inferior frontal dental elements. Results. After the observation was carried out, any of the patients showed periodontal sockets and gingival recession. In the Study Group, only 1 patient had a PI score=3, the 7 left had scores between 0.66 and 2.83. In the Control Group, one patient had score=0, the other ones showed values between 0.5 and 1.66. The mean GI in the Study Group peaked at a score of 2.83, the minimum was 0.66; whereas in the Control Group the maximum value was 2 and the minimum 0.66. The CI in the Group Study was between 1 and 2. In the Control Group it was absent in only 1 patient, whereas in the remaining 7, it had a value between 0.3 and 1. The clinical data were studied by means of the Wilcoxon test. We found a statistically significant difference for what concerns the Plaque Indexes (PI) (P>0.05) and Calculus Indexes (CI) (P>0.1) in both groups, with higher scores in the Study Group, having retainers for 36 months. Any statistically significant difference was calculated for the GI. Conclusions. We can therefore conclude that patients with lingual retainers need periodontal hygiene and treatment as to prevent, in the course of time, periodontal damages non-detectable in short-term

A proposal of quantum-inspired machine learning for medical purposes: An application case

Learning tasks are implemented via mappings of the sampled data set, including both the classical and the quantum framework. Biomedical data characterizing complex diseases such as cancer typically require an algorithmic support for clinical decisions, especially for early stage tumors that typify breast cancer patients, which are still controllable in a therapeutic and surgical way. Our case study consists of the prediction during the pre-operative stage of lymph node metastasis in breast cancer patients resulting in a negative diagnosis after clinical and radiological exams. The classifier adopted to establish a baseline is characterized by the result invariance for the order permutation of the input features, and it exploits stratifications in the training procedure. The quantum one mimics support vector machine mapping in a high-dimensional feature space, yielded by encoding into qubits, while being characterized by complexity. Feature selection is exploited to study the performances associated with a low number of features, thus implemented in a feasible time. Wide variations in sensitivity and specificity are observed in the selected optimal classifiers during cross-validations for both classification system types, with an easier detection of negative or positive cases depending on the choice between the two training schemes. Clinical practice is still far from being reached, even if the flexible structure of quantum-inspired classifier circuits guarantees further developments to rule interactions among features: this preliminary study is solely intended to provide an overview of the particular tree tensor network scheme in a simplified version adopting just product states, as well as to introduce typical machine learning procedures consisting of feature selection and classifier performance evaluation

Radiomic analysis in contrast-enhanced spectral mammography for predicting breast cancer histological outcome

Contrast-Enhanced Spectral Mammography (CESM) is a recently introduced mammographic method with characteristics particularly suitable for breast cancer radiomic analysis. This work aims to evaluate radiomic features for predicting histological outcome and two cancer molecular subtypes, namely Human Epidermal growth factor Receptor 2 (HER2)-positive and triple-negative. From 52 patients, 68 lesions were identified and confirmed on histological examination. Radiomic analysis was performed on regions of interest (ROIs) selected from both low-energy (LE) and ReCombined (RC) CESM images. Fourteen statistical features were extracted from each ROI. Expression of estrogen receptor (ER) was significantly correlated with variation coefficient and variation range calculated on both LE and RC images; progesterone receptor (PR) with skewness index calculated on LE images; and Ki67 with variation coefficient, variation range, entropy and relative smoothness indices calculated on RC images. HER2 was significantly associated with relative smoothness calculated on LE images, and grading tumor with variation coefficient, entropy and relative smoothness calculated on RC images. Encouraging results for differentiation between ER+/ER−, PR+/PR−, HER2+/HER2−, Ki67+/Ki67−, High-Grade/Low-Grade and TN/NTN were obtained. Specifically, the highest performances were obtained for discriminating HER2+/HER2− (90.87%), ER+/ER− (83.79%) and Ki67+/Ki67− (84.80%). Our results suggest an interesting role for radiomics in CESM to predict histological outcomes and particular tumors’ molecular subtype

Relationship between vascular resistance and sympathetic nerve fiber density in arterial vessels in children with sleep disordered breathing

Sleep disordered breathing in children is associated with increased blood flow velocity and sympathetic overactivity. Sympathetic overactivity results in peripheral vasoconstriction and reduced systemic vascular compliance, which increases blood flow velocity during systole. Augmented blood flow velocity is recognized to promote vascular remodeling. Importantly, increased vascular sympathetic nerve fiber density and innervation in early life plays a key role in the development of early-onset hypertension in animal models. Examination of sympathetic nerve fiber density of the tonsillar arteries in children undergoing adenotonsillectomy for Sleep disordered breathing will address this question in humans.Thirteen children scheduled for adenotonsillectomy to treat sleep disordered breathing underwent pupillometry, polysomnography, flow-mediated dilation, resting brachial artery blood flow velocity (velocity time integral), and platelet aggregation. The dorsal lingual artery (tonsil) was stained and immunofluorescence techniques used to determine sympathetic nerve fiber density. Sympathetic nerve fiber density was correlated with increased resting velocity time integral (r=0.63; P<0.05) and a lower Neuronal Pupillary Index (r=-0.71, P<0.01), as well as a slower mean pupillary constriction velocity (mean, r=-0.64; P<0.05). A faster resting velocity time integral was associated with a slower peak pupillary constriction velocity (r=-0.77; P<0.01) and higher platelet aggregation to collagen antigen (r=0.64; P<0.05). Slower mean and peak pupillary constriction velocity were associated with higher platelet aggregation scores (P<0.05; P<0.01, respectively).These results indicate that sympathetic activity is associated with change in both the function and structure of systemic vasculature in children with sleep disordered breathing.Anna Kontos, Kurt Lushington, James Martin, Quenten Schwarz, Ryan Green, Ben, David Wabnitz, Xiangjun Xu, Elke M. Sokoya, Scott Willoughby, Mathias Baumert, Antonio Ferrante, Melissa La Forgia, Declan Kenned

A roadmap towards breast cancer therapies supported by explainable artificial intelligence

In recent years personalized medicine reached an increasing importance, especially in the design of oncological therapies. In particular, the development of patients’ profiling strategies suggests the possibility of promising rewards. In this work, we present an explainable artificial intelligence (XAI) framework based on an adaptive dimensional reduction which (i) outlines the most important clinical features for oncological patients’ profiling and (ii), based on these features, determines the profile, i.e., the cluster a patient belongs to. For these purposes, we collected a cohort of 267 breast cancer patients. The adopted dimensional reduction method determines the relevant subspace where distances among patients are used by a hierarchical clustering procedure to identify the corresponding optimal categories. Our results demonstrate how the molecular subtype is the most important feature for clustering. Then, we assessed the robustness of current therapies and guidelines; our findings show a striking correspondence between available patients’ profiles determined in an unsupervised way and either molecular subtypes or therapies chosen according to guidelines, which guarantees the interpretability characterizing explainable approaches to machine learning techniques. Accordingly, our work suggests the possibility to design data-driven therapies to emphasize the differences observed among the patients

Spectrophotometric investigation of Phobos with the Rosetta OSIRIS-NAC camera and implications for its collisional capture

TheMartian satellite Phobos has been observed on 2007 February 24 and 25, during the pre- and post-Mars closest approach (CA) of the ESA Rosetta spacecraftMars swing-by. The goal of the observations was the determination of the surface composition of different areas of Phobos, in order to obtain new clues regarding its nature and origin. Near-ultraviolet, visible and near-infrared (263.5-992.0 nm) images of Phobos's surface were acquired using the Narrow Angle Camera of the OSIRIS instrument onboard Rosetta. The six multi-wavelength sets of observations allowed a spectrophotometric characterization of different areas of the satellite, belonging respectively to the leading and trailing hemisphere of the anti-Mars hemisphere, and also of a section of its sub-Mars hemisphere. The pre-CA spectrophotometric data obtained with a phase angle of 19° have a spectral trend consistent within the error bars with those of unresolved/disc-integrated measurements present in the literature. In addition, we detect an absorption band centred at 950 nm, which is consistent with the presence of pyroxene. The post-CA observations cover from NUV to NIR a portion of the surface (0° to 43°E of longitude) never studied before. The reflectance measured on our data does not fit with the previous spectrophotometry above 650 nm. This difference can be due to two reasons. First, the OSIRIS observed area in this observation phase is completely different with respect to the other local specific spectra and hence the spectrum may be different. Secondly, due to the totally different observation geometry (the phase angle ranges from 137° to 140°), the differences of spectral slope can be due to phase reddening. The comparison of our reflectance spectra, both pre-and post-CA, with those of D-type asteroids shows that the spectra of Phobos are all redder than the mean D-type spectrum, but within the spectral dispersion of other D-types. To complement this result, we performed an investigation of the conditions needed to collisionally capture Phobos in a way similar to that proposed for the irregular satellites of the giant planets. Once put in the context of the current understanding of the evolution of the early Solar system, the coupled observational and dynamical results we obtained strongly argue for an early capture of Phobos, likely immediately after the formation of Mars. © 2012 The Authors

A Gradient-Based Approach for Breast DCE-MRI Analysis

Breast cancer is the main cause of female malignancy worldwide. Effective early detection by imaging studies remains critical to decrease mortality rates, particularly in women at high risk for developing breast cancer. Breast Magnetic Resonance Imaging (MRI) is a common diagnostic tool in the management of breast diseases, especially for high-risk women. However, during this examination, both normal and abnormal breast tissues enhance after contrast material administration. Specifically, the normal breast tissue enhancement is known as background parenchymal enhancement: it may represent breast activity and depends on several factors, varying in degree and distribution in different patients as well as in the same patient over time. While a light degree of normal breast tissue enhancement generally causes no interpretative difficulties, a higher degree may cause difficulty to detect and classify breast lesions at Magnetic Resonance Imaging even for experienced radiologists. In this work, we intend to investigate the exploitation of some statistical measurements to automatically characterize the enhancement trend of the whole breast area in both normal and abnormal tissues independently from the presence of a background parenchymal enhancement thus to provide a diagnostic support tool for radiologists in the MRI analysis

Prenatal tobacco smoke exposure increases hospitalizations for bronchiolitis in infants

BACKGROUND: Tobacco smoke exposure (TSE) is a worldwide health problem and it is considered a risk factor for pregnant women's and children's health, particularly for respiratory morbidity during the first year of life. Few significant birth cohort studies on the effect of prenatal TSE via passive and active maternal smoking on the development of severe bronchiolitis in early childhood have been carried out worldwide. METHODS: From November 2009 to December 2012, newborns born at ≥ 33 weeks of gestational age (wGA) were recruited in a longitudinal multi-center cohort study in Italy to investigate the effects of prenatal and postnatal TSE, among other risk factors, on bronchiolitis hospitalization and/or death during the first year of life. RESULTS: Two thousand two hundred ten newborns enrolled at birth were followed-up during their first year of life. Of these, 120 (5.4%) were hospitalized for bronchiolitis. No enrolled infants died during the study period. Prenatal passive TSE and maternal active smoking of more than 15 cigarettes/daily are associated to a significant increase of the risk of offspring children hospitalization for bronchiolitis, with an adjHR of 3.5 (CI 1.5-8.1) and of 1.7 (CI 1.1-2.6) respectively. CONCLUSIONS: These results confirm the detrimental effects of passive TSE and active heavy smoke during pregnancy for infants' respiratory health, since the exposure significantly increases the risk of hospitalization for bronchiolitis in the first year of lif