Atypical odontalgia and trigeminal neuralgia: psychological, behavioural and psychopharmacologic approach – an overview of the pathologies related to the challenging differential diagnosis in orofacial pain

Orofacial pain represents a challenge for dentists, especially if with a non-odontogenic basis. Orofacial neuropathic pain is chronic, arduous to localize and develops without obvious pathology. Comorbid psychiatric disorders, such as anxiety and depression, coexist and negatively affect the condition. This article presents one case of atypical odontalgia and one of trigeminal neuralgia treated with psychological and psychopharmacologic tailored and adapted therapies, after conventional medications had failed. In addition, an overview of the pathologies related to the challenging differential diagnosis in orofacial pain is given, since current data are insufficient. A 68-year-old male complained of chronic throbbing, burning pain in a maxillary tooth, worsening upon digital pressure. Symptoms did not abate after conventional amitriptyline therapy; psychological intervention and antianxiety drug were supplemented and antidepressant agent dosage incremented; the patient revealed improvement and satisfaction with the multidisciplinary approach to his pathology. A 72-year-old male lamented chronic stabbing, intermittent, sharp, shooting and electric shock-like pain in an upper tooth, radiating and following the distribution of the trigeminal nerve. Pain did not recur after psychological intervention and a prescription of antidepressant and antianxiety agents, while conventional carbamazepine therapy had not been sufficient to control pain. Due to concern with comorbid psychiatric disorders, we adopted a patient-centered, tailored and balanced therapy, favourably changing the clinical outcome. Comorbid psychiatric disorders have a negative impact on orofacial pain and dentists should consider adopting tailored therapies, such as psychological counselling and behavioural and psychopharmacologic strategies, besides conventional treatments. They also need to be familiar with the signs and symptoms of orofacial pain, recollecting a comprehensive view of the pathologies concerning the differential diagnosis. A prompt diagnosis prevents pain chronicity, avoiding an increase in complexity and a shift to orofacial neuropathic pain and legal claims

A case series analysing patients with dental anxiety: a patient-centred model based on psychological profiling

Anxiety and distress can jeopardize dental care experience of patients and may affect the clinical result. Although a wide range of sedation and analgesia techniques are currently available to relieve distress and pain during dental procedures, operative models to choose the most effective sedation-analgesic strategies are lacking. This case series proposes a patient-centred model to optimize patients' cooperation during dental care delivery. We describe how to achieve correct anaesthesia by using the least sedative procedure, accounting for the dental procedure needed and patient's psychological profile. Five patients were considered as paradigmatic to show the balance between patients' subjective experiences and the clinical procedures: a patient with low stress, good compliance (case 1); moderate stress and reduction in compliance (case 2); anxious patient (case 3); patient with acute anxiety and emotional distress (case 4); anguished patient (case 5). A multimodal treatment of emotional and behavioural condition and a patient-centred model approach contributed to achieve the best patient satisfaction in the five cases detailed here

On the use of human mobility proxy for the modeling of epidemics

Human mobility is a key component of large-scale spatial-transmission models of infectious diseases. Correctly modeling and quantifying human mobility is critical for improving epidemic control policies, but may be hindered by incomplete data in some regions of the world. Here we explore the opportunity of using proxy data or models for individual mobility to describe commuting movements and predict the diffusion of infectious disease. We consider three European countries and the corresponding commuting networks at different resolution scales obtained from official census surveys, from proxy data for human mobility extracted from mobile phone call records, and from the radiation model calibrated with census data. Metapopulation models defined on the three countries and integrating the different mobility layers are compared in terms of epidemic observables. We show that commuting networks from mobile phone data well capture the empirical commuting patterns, accounting for more than 87% of the total fluxes. The distributions of commuting fluxes per link from both sources of data - mobile phones and census - are similar and highly correlated, however a systematic overestimation of commuting traffic in the mobile phone data is observed. This leads to epidemics that spread faster than on census commuting networks, however preserving the order of infection of newly infected locations. Match in the epidemic invasion pattern is sensitive to initial conditions: the radiation model shows higher accuracy with respect to mobile phone data when the seed is central in the network, while the mobile phone proxy performs better for epidemics seeded in peripheral locations. Results suggest that different proxies can be used to approximate commuting patterns across different resolution scales in spatial epidemic simulations, in light of the desired accuracy in the epidemic outcome under study.Comment: Accepted fro publication in PLOS Computational Biology. Abstract shortened to fit Arxiv limits. 35 pages, 6 figure

Lack of SARS-CoV-2 RNA environmental contamination in a tertiary referral hospital for infectious diseases in Northern Italy

none140noNAnoneColaneri M.; Seminari E.; Piralla A.; Zuccaro V.; Di Filippo A.; Baldanti F.; Bruno R.; Mondelli M.U.; Brunetti E.; Di Matteo A.; Maiocchi L.; Pagnucco L.; Mariani B.; Ludovisi S.; Lissandrin R.; Parisi A.; Sacchi P.; Patruno S.F.A.; Michelone G.; Gulminetti R.; Zanaboni D.; Novati S.; Maserati R.; Orsolini P.; Vecchia M.; Sciarra M.; Asperges E.; Sambo M.; Biscarini S.; Lupi M.; Roda S.; Chiara Pieri T.; Gallazzi I.; Sachs M.; Valsecchi P.; Perlini S.; Alfano C.; Bonzano M.; Briganti F.; Crescenzi G.; Giulia Falchi A.; Guarnone R.; Guglielmana B.; Maggi E.; Martino I.; Pettenazza P.; Pioli di Marco S.; Quaglia F.; Sabena A.; Salinaro F.; Speciale F.; Zunino I.; De Lorenzo M.; Secco G.; Dimitry L.; Cappa G.; Maisak I.; Chiodi B.; Sciarrini M.; Barcella B.; Resta F.; Moroni L.; Vezzoni G.; Scattaglia L.; Boscolo E.; Zattera C.; Michele Fidel T.; Vincenzo C.; Vignaroli D.; Bazzini M.; Iotti G.; Mojoli F.; Belliato M.; Perotti L.; Mongodi S.; Tavazzi G.; Marseglia G.; Licari A.; Brambilla I.; Daniela B.; Antonella B.; Patrizia C.; Giulia C.; Giuditta C.; Marta C.; Rossana D.; Milena F.; Bianca M.; Roberta M.; Enza M.; Stefania P.; Maurizio P.; Elena P.; Antonio P.; Francesca R.; Antonella S.; Maurizio Z.; Guy A.; Laura B.; Ermanna C.; Giuliana C.; Luca D.; Gabriella F.; Gabriella G.; Alessia G.; Viviana L.; Claudia L.; Valentina M.; Simona P.; Marta P.; Alice B.; Giacomo C.; Irene C.; Alfonso C.; Di Martino R.; Di Napoli A.; Alessandro F.; Guglielmo F.; Loretta F.; Federica G.; Alessandra M.; Federica N.; Giacomo R.; Beatrice R.; Maria S.I.; Monica T.; Nepita Edoardo V.; Calvi M.; Tizzoni M.; Nicora C.; Triarico A.; Petronella V.; Marena C.; Muzzi A.; Lago P.; Comandatore F.; Bissignandi G.; Gaiarsa S.; Rettani M.; Bandi C.Colaneri, M.; Seminari, E.; Piralla, A.; Zuccaro, V.; Di Filippo, A.; Baldanti, F.; Bruno, R.; Mondelli, M. U.; Brunetti, E.; Di Matteo, A.; Maiocchi, L.; Pagnucco, L.; Mariani, B.; Ludovisi, S.; Lissandrin, R.; Parisi, A.; Sacchi, P.; Patruno, S. F. A.; Michelone, G.; Gulminetti, R.; Zanaboni, D.; Novati, S.; Maserati, R.; Orsolini, P.; Vecchia, M.; Sciarra, M.; Asperges, E.; Sambo, M.; Biscarini, S.; Lupi, M.; Roda, S.; Chiara Pieri, T.; Gallazzi, I.; Sachs, M.; Valsecchi, P.; Perlini, S.; Alfano, C.; Bonzano, M.; Briganti, F.; Crescenzi, G.; Giulia Falchi, A.; Guarnone, R.; Guglielmana, B.; Maggi, E.; Martino, I.; Pettenazza, P.; Pioli di Marco, S.; Quaglia, F.; Sabena, A.; Salinaro, F.; Speciale, F.; Zunino, I.; De Lorenzo, M.; Secco, G.; Dimitry, L.; Cappa, G.; Maisak, I.; Chiodi, B.; Sciarrini, M.; Barcella, B.; Resta, F.; Moroni, L.; Vezzoni, G.; Scattaglia, L.; Boscolo, E.; Zattera, C.; Michele Fidel, T.; Vincenzo, C.; Vignaroli, D.; Bazzini, M.; Iotti, G.; Mojoli, F.; Belliato, M.; Perotti, L.; Mongodi, S.; Tavazzi, G.; Marseglia, G.; Licari, A.; Brambilla, I.; Daniela, B.; Antonella, B.; Patrizia, C.; Giulia, C.; Giuditta, C.; Marta, C.; D'Alterio, Rossana; Milena, F.; Bianca, M.; Roberta, M.; Enza, M.; Stefania, P.; Maurizio, P.; Elena, P.; Antonio, P.; Francesca, R.; Antonella, S.; Maurizio, Z.; Guy, A.; Laura, B.; Ermanna, C.; Giuliana, C.; Luca, D.; Gabriella, F.; Gabriella, G.; Alessia, G.; Viviana, L.; Meisina, Claudia; Valentina, M.; Simona, P.; Marta, P.; Alice, B.; Giacomo, C.; Irene, C.; Alfonso, C.; Di Martino, R.; Di Napoli, A.; Alessandro, F.; Guglielmo, F.; Loretta, F.; Federica, G.; Albertini, Alessandra; Federica, N.; Giacomo, R.; Beatrice, R.; Maria, S. I.; Monica, T.; Nepita Edoardo, V.; Calvi, M.; Tizzoni, M.; Nicora, C.; Triarico, A.; Petronella, V.; Marena, C.; Muzzi, A.; Lago, P.; Comandatore, F.; Bissignandi, G.; Gaiarsa, S.; Rettani, M.; Bandi, C

Clinical characteristics of coronavirus disease (COVID-19) early findings from a teaching hospital in Pavia, North Italy, 21 to 28 February 2020

We describe clinical characteristics, treatments and outcomes of 44 Caucasian patients with coronavirus disease (COVID-19) at a single hospital in Pavia, Italy, from 21\u201328 February 2020, at the beginning of the outbreak in Europe. Seventeen patients developed severe disease, two died. After a median of 6 days, 14 patients were discharged from hospital. Predictors of lower odds of discharge were age>65 years, antiviral treatment and for severe disease, lactate dehydrogenase >300 mg/dL

New technique for inter-implant papilla reconstruction between two or more implants in patients with variably reabsorbed ridges and flat anatomy. Preliminary results of a 9 consecutive clinical case series

The aim of the work: Interimplant papilla reconstruction is difficult because the biologic width around an implant is apical to the implant-abutment connection and because the biologic width creates subcrestally. The aim of this study is to investigate whether the reconstruction of the interimplant papilla can be achieved by the use of an innovative surgical technique combined with scalloped implants, in the most severe surgical conditions, i.e. in variably reabsorbed ridges with flat anatomy. Materials and method: Nine surgical sites, in eight consecutive patients, were treated with at least two adjacent scalloped implants and fixed prosthesis. 23 scalloped implants were placed using this new surgical technique on bone and soft tissue structures. One flat platform implant was also placed between two other scalloped implants. A total of 15 interimplant papillae were examined. Results: 100% of papilla reconstruction at first prosthesis insertion. 13.3% failed to maintain interimplant papillae after 6 months and 20% after 12 months. Also, papilla reconstruction was maintained for 12 months in the mesial and distal embrasure spaces of the flat platform implant. Conclusion: The combination of the use of adjacent scalloped implants with this surgical approach, even in reabsorbed ridges with flat anatomy, may reform inter-implant bone peaks as support for the papillae

Epidemic invasion trees.

<p>The full invasion trees for are shown for Portugal (top row) and France (bottom row) in the cases of the census network (<b>a, d</b>), the mobile phone network (<b>b, e</b>) and the radiation network (<b>c, f</b>). Seeds of the simulations (black nodes) are Lisbon for Portugal and Barcelonnette for France. Nodes belonging to the first shell of the tree, i.e. those directly infected from the seed are fully colored. Grey nodes have been infected by secondary infected nodes.</p

Basic properties of the commuting networks.

<p>Number of nodes, of links, and of commuters for each commuting network under study, without considering self-loops. Rows correspond to different countries and geographical subdivisions within a country. Columns indicate values from the census dataset and the mobile phone dataset. Commuters for the mobile phone dataset correspond to the values obtained directly from the samples, prior to the normalization procedure, and after the basic normalization procedure. Values obtained with the refined normalization are not reported as they are equal to those of the census dataset, by definition.</p

Epidemic spreading.

<p>Comparing the epidemic behavior on the census network and two proxy networks, mobile phone (red symbols) and radiation model (blue symbols), in Portugal (top panels), Spain (middle) and France (bottom). <b>a, d, g</b> Jaccard similarity index measured between the epidemic infection tree of the census network and the infection tree of the proxy network, for three values of the basic reproduction number . Each symbol corresponds to a different initial infection seed, displayed on the map (right panels). <b>b, e, h</b> Differences between the arrival times in the census network and in the proxy network, for different values of and infection seed. Box plots indicate the 90% reference range, measured on all the network nodes. <b>c, f, i</b> Comparing the arrival times in the mobile phone network with those in the census network , for and the epidemic starting from the capital city. Red points are scatter plot for each node of the network and we subtracted the average systematic difference from each .</p

Comparing the weights of the census networks and the mobile phone networks.

<p>Top: probability density distributions of the weights () of the census commuting network (grey) and the mobile phone commuting network (red) in Portugal (<b>a</b>), Spain (<b>b</b>) and France (<b>c</b>). Bottom: comparing weights in the mobile phone network () and weights in the census networks () in Portugal (<b>d</b>), Spain (<b>e</b>) and France (<b>f</b>). Grey points are scatter plot for each connection. Box plots indicate the 95% reference range of values within a bin.</p