Background: The present study analyzed the changes obtained on the upper airway and hyoid bone dimensions in a group of patients with skeletal Class II malocclusion treated with functional elastodontic devices compared to an untreated control group. Methods: A group of 33 patients (19 females and 14 males) with Class II malocclusion treated with AMCOP^® SC elastodontic device was compared with a control group of 35 subjects (17 females and 18 males) with untreated Class II malocclusion. Lateral cephalograms were available at the start (T0) and end of treatment/period of observation (T1). Cephalometric analysis was performed and linear measurements to evaluate airway space and hyoid bone position were also obtained. A multivariate analysis of variance for repeated measures (MANOVA) was performed to determine the effects of interactions for the groups for time. Results: Statistically significant differences were found in the study group from T0 and T1 with an improvement of superior upper airway (SPAS p < 0.0001), while in the control group it did not change in a statistically significant way from T0 to T1. The MANOVA test showed statistically significant differences between the two groups for the changes of SPAS (p = 0.003), IAS (p = 0.049), and H-C3 vertical (p = 0.038) values. Conclusions: Functional elastodontic therapy produced significant favorable airway changes in skeletal class II subjects.

Italy was one of the nations most affected by SARS-CoV-2. During the pandemic period, the national government approved some restrictions to reduce diffusion of the virus. We aimed to evaluate changes in in-hospital mortality and its possible relation with patient comorbidities and different restrictive public health measures adopted during the 2020 pandemic period. We analyzed the hospital discharge records of inpatients from public and private hospitals in Apulia (Southern Italy) from 1 January 2019 to 31 December 2020. The study period was divided into four phases according to administrative restriction. The possible association between in-hospital deaths, hospitalization period, and covariates such as age group, sex, Charlson comorbidity index (CCI) class, and length of hospitalization stay (LoS) class was evaluated using a multivariable logistic regression model. The risk of death was slightly higher in men than in women (OR 1.04, 95% CI: 1.01–1.07) and was lower for every age group below the >75 years age group. The risk of in-hospital death was lower for hospitalizations with a lower CCI score. In summary, our analysis shows a possible association between in-hospital mortality in non-COVID-19-related diseases and restrictive measures of public health. The risk of hospital death increased during the lockdown period.

The aim of this study was to investigate the spatiotemporal association between socioeconomic deprivation and the incidence of COVID-19 and how this association changes through the seasons due to the existence of restrictive public health measures. A retrospective observational study was conducted among COVID-19 cases that occurred in the Apulia region from 29 February 2020 to 31 December 2021, dividing the period into four phases with different levels of restrictions. A generalized estimating equation (GEE) model was applied to test the independent effect of deprivation on the incidence of COVID-19, taking into account age, sex, and regional incidence as possible confounding effects and covariates, such as season and levels of restrictions, as possible modifying effects. The highest incidence was in areas with a very high deprivation index (DI) in winter. During total lockdown, no rate ratio between areas with different levels of DI was significant, while during soft lockdown, areas with very high DI were more at risk than all other areas. The effects of social inequalities on the incidence of COVID-19 changed in association with the seasons and restrictions on public health. Disadvantaged areas showed a higher incidence of COVID-19 in the cold seasons and in the phases of soft lockdown.

Many studies have identified predictors of outcomes for inpatients with coronavirus disease 2019 (COVID-19), especially in intensive care units. However, most retrospective studies applied regression methods to evaluate the risk of death or worsening health. Recently, new studies have based their conclusions on retrospective studies by applying machine learning methods. This study applied a machine learning method based on decision tree methods to define predictors of outcomes in an internal medicine unit with a prospective study design. The main result was that the first variable to evaluate prediction was the international normalized ratio, a measure related to prothrombin time, followed by immunoglobulin M response. The model allowed the threshold determination for each continuous blood or haematological parameter and drew a path toward the outcome. The model’s performance (accuracy, 75.93%; sensitivity, 99.61%; and specificity, 23.43%) was validated with a k-fold repeated cross-validation. The results suggest that a machine learning approach could help clinicians to obtain information that could be useful as an alert for disease progression in patients with COVID-19. Further research should explore the acceptability of these results to physicians in current practice and analyze the impact of machine learning-guided decisions on patient outcomes.

Introduction: Molnupiravir and Nirmatrelvir/r (NMV-r) have been proven to reduce severe Coronavirus Disease 2019 (COVID-19) in unvaccinated high-risk individuals. Data regarding their impact in fully vaccinated vulnerable subjects with mild-to-moderate COVID-19 are still limited, particularly in the era of Omicron and sub-variants. Methods: Our retrospective study aimed to compare the safety profile and effectiveness of the two antivirals in all consecutive high-risk outpatients between 11 January and 10 July 2022. A logistic regression model was carried out to assess factors associated with the composite outcome defined as all-cause hospitalization and/or death at 30 days. Results: A total of 719 individuals were included: 554 (77%) received Molnupiravir, whereas 165 (23%) were NMV-r users. Overall, 43 all-cause hospitalizations (5.9%) and 13 (1.8%) deaths were observed at 30 days. A composite outcome occurred in 47 (6.5%) individuals. At multivariate analysis, male sex [OR 3.785; p = 0.0021], age ≥ 75 [OR 2.647; p = 0.0124], moderate illness [OR 16.75; p < 0.001], and treatment discontinuation after medical decision [OR 8.148; p = 0.0123] remained independently associated with the composite outcome. Conclusions: No differences between the two antivirals were observed. In this real-life setting, the early use of both of the oral antivirals helped limit composite outcome at 30 days among subjects who were at high risk of disease progression.

Introduction: Sleep disorders have been reported in individuals living with HIV (PLWH), with a prevalence rate of over 50%. The main risk factors contributing to the development of sleep disturbances are not yet fully understood. We investigate the prevalence and risk factors associated with poor sleep quality in a population of PLWH who are receiving antiretroviral therapy (ART). Methods: The Pittsburgh Sleep Quality Index (PSQI) was used to evaluate sleep quality in PLWH attending our HIV Outpatient Clinic between October 2022 and April 2023. All subjects with a PSQI score > 5 were considered bad sleepers. A logistic regression analysis was carried out to assess risk factors associated with a PSQI score > 5. Results: A total of 132 PLWH (78% males) who received ART for at least one month were included in this observational study. The median age was 56 (IQR 47–61). Among all, 41 (31%) had a history of AIDS, and 95 (72%) were receiving an INSTI-based ART. The study population was divided into two groups: PSQI ≤ 5 (90; 68.2%) and PSQI > 5 (42; 31.8%). A lower BMI and the use of bictegravir in the current ART were associated with a PSQI score ≤ 5. In the multivariate analysis, the use of a bictegravir-based ART remained the only factor associated with better sleep quality (OR 0.17; p = 0.0222). No further associations between sleep disturbances and other epidemiological and clinical features were found. Conclusion: In this real-life scenario, poor sleep quality was observed in 31% of the cases, primarily among individuals with higher BMI. In addition, bictegravir users might seem to have a lower likelihood of experiencing poor sleep quality.