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Objectives: The aim of this study was to evaluate esthetic parameters in the anterior maxillary region by comparing single‐piece zirconia versus titanium narrow‐diameter implants. Additionally, clinical, radiological and patient‐reported outcome measures (PROMs) were analyzed. Materials and Methods: Thirty implants (tissue level implant) were placed in 30 patients in the maxillary esthetic sector. Depending on randomization, a zirconia (test) or titanium implant (control) was placed. Esthetic, clinical, and radiological parameters, including the implant crown esthetic index (ICAI), pink esthetic score (PES), probing pocket depth, bleeding on probing, plaque index, and marginal bone levels, were evaluated at 12, 36 and 60 months after loading. Results: Sixty months after crown placement, no significant differences were found between groups. The ICAI values were 5.25 ± 4.21 and 4.50 ± 2.98 for the test and control groups, respectively. The corresponding PES values were 7.44 ± 1.93 and 7.43 ± 1.74 for the test and control groups, respectively. There were no significant intergroup differences for the rest of the parameters evaluated. Conclusion: It can be suggested that monotype zirconia implants may serve as a potential alternative to titanium implants in selected clinical scenarios. While the results demonstrated comparable esthetic, clinical, and radiological aspects for zirconia implants as compared to titanium implants after a 5‐year follow‐up period, further research with larger sample sizes and longer‐term follow‐up is recommended. [ABSTRACT FROM AUTHOR]

This case report presents a novel approach for root coverage of multiple gingival recessions with a volume-stable collagen matrix functionalized with injectable platelet-rich fibrin (i-PRF). A patient with multiple gingival recessions in the anterior maxilla was submitted to root coverage by coronally advanced flap with split–full–split incisions. Blood collection was performed before surgery and i-PRF was obtained after centrifugation (relative centrifugal force (RCF) 400 g, 2700 rpm, 3 minutes). A volume-stable collagen matrix was soaked with i-PRF and applied as a substitute for autogenous connective tissue graft. A mean root coverage of 83% was observed after a 12-month follow-up period, and only slight modifications were detected in a 30-month follow-up consultation. The association of a volume-stable collagen matrix with i-PRF successfully treated multiple gingival recessions with reduced morbidity since a connective tissue collection was avoided.

BackgroundDonor availability and transplantation-related risks limit the broad use of allogeneic hematopoietic-cell transplantation in patients with transfusion-dependent β-thalassemia. After previously establishing that lentiviral transfer of a marked β-globin (βA-T87Q) gene could substitute for long-term red-cell transfusions in a patient with β-thalassemia, we wanted to evaluate the safety and efficacy of such gene therapy in patients with transfusion-dependent β-thalassemia.MethodsIn two phase 1-2 studies, we obtained mobilized autologous CD34+ cells from 22 patients (12 to 35 years of age) with transfusion-dependent β-thalassemia and transduced the cells ex vivo with LentiGlobin BB305 vector, which encodes adult hemoglobin (HbA) with a T87Q amino acid substitution (HbAT87Q). The cells were then reinfused after the patients had undergone myeloablative busulfan conditioning. We subsequently monitored adverse events, vector integration, and levels of replication-competent lentivirus. Efficacy assessments included levels of total hemoglobin and HbAT87Q, transfusion requirements, and average vector copy number.ResultsAt a median of 26 months (range, 15 to 42) after infusion of the gene-modified cells, all but 1 of the 13 patients who had a non-β0/β0 genotype had stopped receiving red-cell transfusions; the levels of HbAT87Q ranged from 3.4 to 10.0 g per deciliter, and the levels of total hemoglobin ranged from 8.2 to 13.7 g per deciliter. Correction of biologic markers of dyserythropoiesis was achieved in evaluated patients with hemoglobin levels near normal ranges. In 9 patients with a β0/β0 genotype or two copies of the IVS1-110 mutation, the median annualized transfusion volume was decreased by 73%, and red-cell transfusions were discontinued in 3 patients. Treatment-related adverse events were typical of those associated with autologous stem-cell transplantation. No clonal dominance related to vector integration was observed.ConclusionsGene therapy with autologous CD34+ cells transduced with the BB305 vector reduced or eliminated the need for long-term red-cell transfusions in 22 patients with severe β-thalassemia without serious adverse events related to the drug product. (Funded by Bluebird Bio and others; HGB-204 and HGB-205 ClinicalTrials.gov numbers, NCT01745120 and NCT02151526 .).

Education in the countryside is a result of historical fights for the rights of property, education, social justice, and labor. The COVID-19 Pandemic highlighted the existing vulnerabilities and precariousness, and alongside the social isolation came the need to cope with mental health issues and psychosocial support for countryside teachers. This paper aims to present an analysis on the implications of COVID-19 Pandemic in mental and psychosocial health of the countryside teachers. Five countryside education professionals were interviewed, one from each Brazilian region. Online semi-structured interviews were analysed by discourse analysis. The findings showed situations leading to psychological distress as well as critical protective processes, which promoted mental health in the pandemic context. Pedagogical practices from distance have turned the situation of countryside education and its educators even more precarious and vulnerable. The pandemic accelerated processes of depersonalisation and devaluation of countryside education.

RESUMO Em março/2020, a Organização Mundial da Saúde emitiu a declaração de pandemia, em decorrência da disseminação do vírus SARS-CoV-2, disparando o alerta mundial sobre a necessidade de rápida expansão da capacidade de vigilância, prevenção e implementação das estruturas de assistência dos sistemas de saúde dos países. No dia seguinte à declaração do Ministério da Saúde que reconheceu a transmissão comunitária no Brasil, um grupo de pesquisadores foi convidado a reunir evidências científicas e melhores práticas de Saúde Mental e Atenção Psicossocial (SMAPS) na Covid-19. Foram selecionados 1.563 artigos que se referiam à saúde mental e/ou atenção psicossocial em contextos de emergências sanitárias. Participaram pesquisadores, docentes e voluntários de 25 instituições reconhecidas pelo notório saber, totalizando um montante de 117 profissionais voluntários. Optou-se por conformar um curso nacional na modalidade de Educação a Distância. Ao todo, 60.780 profissionais ingressaram no curso nos primeiros 30 dias de inscrição. A modelagem on-line, síncrona e assíncrona, possibilitou a formação durante o período de distanciamento social, assim como permitiu que profissionais de todos os estados, e do Distrito Federal, pudessem ter acesso a informações atualizadas e baseadas em pesquisas nacionais e internacionais, buscando garantir o fortalecimento das ações no âmbito das políticas de saúde, e com base nos protocolos internacionais.

The most common subtype of ovarian cancer (OC) is the high-grade serous ovarian carcinoma (HGSOC), accounting for 70%–80% of all OC deaths. Although HGSOC is a potentially immunogenic tumor, clinical studies assessing the effectiveness of inhibitors of programmed death protein and its ligand (PD-1/PD-L1) in OC patients so far showed only response rates 401 U/ml and each soluble protein above respective concentration cutoff were covariates associated with shorter PFS (

RESUMO O campus da Universidade Federal de São Carlos do município de Sorocaba é composto por um remanescente florestal caracterizado por vegetação de mata estacional semidecidual e cerrado. Foram encontradas 96 espécies de briófitas na área: 56 musgos (sendo Fissidentaceae a família mais rica, com 11 spp.) e 40 hepáticas (sendo Lejeuneaceae a mais representativa, com 21 spp.). Oito espécies são endêmicas do Brasil e duas foram citadas pela primeira vez no Estado de São Paulo (Fissidens allionii Broth. e Lejeunea subsessilis Spruce). O predomínio de espécies corticícolas e da forma de vida em tapete indicam a forte relação com as condições microclimáticas do ambiente, assim como a disponibilidade de substrato. A presença simultânea de espécies da Mata Atlântica e do Cerrado indica uma área de transição, ao mesmo tempo que espécies de ampla distribuição, subcosmopolitas e ruderais expressam o caráter secundário do remanescente florestal.

The Kivircik is an indigenous sheep breed from Turkey, and it has superior meat quality compared to other indigenous breeds. Therefore, farmers prioritize Kivircik lamb fattening instead of milk production. Here, we aimed to determine the best nonlinear growth model, i.e., Gompertz, Logistic, Von Bertalanffy, and Brody, to describe the growth curve of Kivircik lambs. The body weight data from birth until 150 days of age belonging to 612 lambs were used as the material of this study. The best fitting model was selected by considering the adjusted coefficient of determination (R2adj), residual mean square, and Akaike’s (AIC) and Bayesian information criteria (BIC). Even though the Brody model had a better statistical fit, considering its biological interpretation, the Gompertz model was identified as an appropriate model for describing Kivircik lamb growth. Male lambs, twin lambs, and lambs born in winter had higher mature live weights (44.2 kg, 71.2 kg, and 38.5 kg, respectively) and rate of weight gain (2.1, 2.6, and 2.0, respectively). However, our subgroups revealed a similar rate of maturity (0.01). Growth models are important tools for deciding the optimal slaughter age and they provide valuable information on the management practices of both sexes, birth types, and birth seasons. These results can be applied to breeding programs for early selection, enabling intervention strategies when needed.

Maize silage suffers from several factors that affect the final quality and, to some extent, pre-ensiled conditions that can be potentially tuned during harvesting. After assessing new indices for silage quality under lab-scale conditions, several trials have been conducted to find associations between fresh maize characteristics and silage features. Among the first, we included field input levels, FAO class, maturity stage, use of bacterial inoculants, sealing delay and chemical traits, whereas, among the latter, we assessed density and porosity, pH, fermentative profile, dry matter loss and aerobic stability. The trials were conducted using vacuum bags or mini silo buckets. More than 1500 maize samples harvested in Northeast Italy were analysed during the 2016–2022 period. Moreover, to evaluate silage aerobic stability, the fermentative profile and temperature were measured 14 days after the opening of the silo. The association between silage quality and aerobic stability was assessed, and a prognostic risk score was used to calculate the probability of aerobic instability. The dataset could provide baseline information to promote the continuous improvement of maize silage management from different botanical and crop fields, thus improving agronomic and animal farm resource allocation from a precision agriculture perspective.

The lack of annotated semantic segmentation datasets for electrical substations in the literature poses a significant problem for machine learning tasks; before training a model, a dataset is needed. This paper presents a new dataset of electric substations with 1660 images annotated with 15 classes, including insulators, disconnect switches, transformers and other equipment commonly found in substation environments. The images were captured using a combination of human, fixed and AGV-mounted cameras at different times of the day, providing a diverse set of training and testing data for algorithm development. In total, 50,705 annotations were created by a team of experienced annotators, using a standardized process to ensure accuracy across the dataset. The resulting dataset provides a valuable resource for researchers and practitioners working in the fields of substation automation, substation monitoring and computer vision. Its availability has the potential to advance the state of the art in this important area.

Fe2+-doped ZnSe nanoparticles, with varying concentrations of Fe2+ dopants, were prepared by the hydrothermal method and investigated using a multi-technique approach exploiting scanning electron microscopy (SEM), X-ray diffraction (XRD), and Raman spectroscopy, as well as measurement of the electrical transport properties and Seebeck coefficient (S). The doped nanoparticles appeared as variable-sized agglomerates on nanocrystallites upon SEM investigation for any doping level. Combined XRD and Raman analyses revealed the occurrence of a cubic structure in the investigated samples. Electric and thermoelectric (TE) transport investigations showed an increase in TE performance with an increase in Fe atom concentrations, which resulted in an enhancement of the power factors from 13 µWm−1K−2 to 120 µWm−1K−2 at room temperature. The results were also dependent on the operating temperature. The maximum power factor of 9 × 10−3 Wm−1K−2 was achieved at 150 °C for the highest explored doping value. The possible applications of these findings were discussed.

The accurate detection of behavioural changes represents a promising method of detecting the early onset of disease in dairy cows. This study assessed the performance of deep learning (DL) in classifying dairy cows’ behaviour from accelerometry data acquired by single sensors on the cows’ left flanks and compared the results with those obtained through classical machine learning (ML) from the same raw data. Twelve cows with a tri-axial accelerometer were observed for 136 ± 29 min each to detect five main behaviours: standing still, moving, feeding, ruminating and resting. For each 8 s time interval, 15 metrics were calculated, obtaining a dataset of 211,720 observation units and 15 columns. The entire dataset was randomly split into training (80%) and testing (20%) datasets. The DL accuracy, precision and sensitivity/recall were calculated and compared with the performance of classical ML models. The best predictive model was an 8-layer convolutional neural network (CNN) with an overall accuracy and F1 score equal to 0.96. The precision, sensitivity/recall and F1 score of single behaviours had the following ranges: 0.93–0.99. The CNN outperformed all the classical ML algorithms. The CNN used to monitor the cows’ conditions showed an overall high performance in successfully predicting multiple behaviours using a single accelerometer.

Infrared thermography is a predictive maintenance tool used in substations to identify a disturbance in electrical equipment that could lead to poor operation and potential failure in the future. According to Joule’s law, the temperature of electrical equipment is proportional to the current flowing through it. Other external factors, such as solar incidence, air humidity, wind speed, and air temperature, can interfere with its operating temperatures. Based on this premise, this article aims to analyze the influence of atmospheric and load conditions on the operational cycle of thermography-monitored equipment in order to describe the operating temperature of the object using only external data and to show the impacts of external influences on the final temperature reached by the object. Five multivariate time series regression models were developed to describe the maximum equipment temperature. The final model achieved the best fit between the measured and model temperature based on the Akaike information criterion (AIC) metric, where all external variables were used to compose the model. The proposed model shows the impacts of each external factor on equipment temperature and could be used to create a predictive maintenance strategy for power substations to avoid failure.