Is There a Relationship between Laser Therapy and Root Canal Cracks Formation? A Systematic Review

Introduction: Crack formation has become an important issue for endodontists, as it can be decisive for the long-term prognosis of the endodontically treated tooth. Since the applicability of laser in endodontics has become frequent, this systematic review aimed to evaluate the association between laser therapy and the formation of cracks in the dentinal structure of the root canal. Materials and Methods: A search was performed in PubMed, Scopus, Web of Science, and Virtual Health Library, as well as in the gray literature, on September 24, 2021. Studies that evaluated the formation of cracks in human root dentin due to different types of lasers were included. The risk of bias was assessed following the modified version of the Consolidated Standards of Reporting Trials (CONSORT) checklist tool. A meta-analysis was performed to evaluate (i) the total number of crack incidences; (ii) complete crack formation; (iii) incomplete crack formation; (iv) intra-dentinal crack formation between ultrasonic tips and laser use. The mean difference was calculated with a 95% confidence interval in a fixed-effect model, the heterogeneity was tested using the I2 index with level of significance of 5%. Results: Of the 22 studies included in this review, 15 have shown that lasers can form cracks in root dentin, including those that performed baseline assessment of samples. The meta-analysis confirmed no difference in crack formation between ultrasonic tips and laser devices. Conclusions: Laser therapy has been gaining prominence in endodontics and that irradiation can form and propagate cracks in the dentinal structure of the root canal assessed by in vitro studies. This is a critical concern for endodontists as it affects the strength and longevity of the tooth. Future research is encouraged to seek the standardization of good methodological practices and achieve establishing parameters to minimize harmful effects of laser on dentin

Craniofaringioma adamantinomatoso: Adamantinomatous craniopharyngioma

Introdução: Craniofaringiomas Adamantinomatosos são tumores do Sistema Nervoso Central, localizados no ducto craniofaríngeo. Provenientes de células escamosas e com curso tipicamente benigno, tem prevalência na infância e incidência até, aproximadamente, os 20 anos de idade. O diagnóstico é frequentemente tardio pelo seu crescimento lentificado e clínica inespecífica, agrupada em: cefaléia, distúrbios visuais e de caráter hormonal. O controle sintomático pode ser realizado farmacologicamente, embora a localização anatômica favoreça uma abordagem cirúrgica para resolução da patologia, considerando, também, tratamento adjuvante. Apresentação do caso: Paciente do sexo feminino, 9 anos de idade, estudante e natural de Rio Verde - GO, é levado pela mãe ao pediatra, que relata que o paciente tem apresentado cefaleia holocraniana intermitente e sem fator causal específico, há aproximadamente 70 dias. Associado ao quadro, refere-se a ganho de peso sem mudanças significativas na dieta ou hábitos de vida, no entanto, não soube especificar o ganho em quilogramas. Foi solicitada Ressonância Magnética de crânio, que confirmou o diagnóstico de Craniofaringioma Adamantinomatoso. Discussão: O desenvolvimento das técnicas cirúrgicas para ressecção tumoral permite optar por uma ressecção completa ou subtotal associada à radioterapia adjuvante (RT), sendo que a segunda permitiu uma taxa maior de sobrevida livre de progressão de doença. Porém, não pode-se excluir a primeira opção, já que é preconizada para indicações específicas, sobretudo diante da localização tumoral. Portanto, a abordagem do tumor deve ser planejada de modo individualizado, já que há risco de prejuízo na qualidade de vida e funcionalidade do indivíduo, em decorrência do sítio patológico e estruturas potencialmente afetadas. Conclusão: Para estabelecer o tratamento correto deve-se observar a localização e os impactos de cada intervenção avaliando as particularidades de cada paciente

Linfoma de Hodgkin com manifestações pulmonares exclusivas: Hodgkin's lymphoma with unique pulmonary manifestations

Introdução:  O envolvimento pulmonar associado ao linfoma de Hodgkin pode ocorrer tanto de forma primária como secundária. A primária é uma entidade rara e origina-se do tecido linfóide associado a mucosa. Já a secundária, mais frequente, pode resultar da proliferação direta dos gânglios linfáticos mediastinais ou de disseminação linfática ou hematogênica de outros locais. Apresentação do caso: Paciente do sexo feminino, 42 anos, com quadro de tosse, odinofagia, dor em seios da face. Há 7 dias iniciou quadro de dispneia e febre, com piora nos últimos 3 dias. Procurou pronto atendimento diversas vezes, com diagnóstico e tratamento de sinusite. Foi realizado Rx de seios da face, sem alterações. Hemograma completo, VHS, teste para tuberculose, sem achados específicos. Discussão: O linfoma Hodgkin pulmonar primário é uma patologia incomum, com poucos casos documentados, consistindo em menos de 1% de todos os linfomas. Apresenta discreta preponderância de incidência em mulheres (1,4:1 F:M) com distribuição bimodal de idade (<35 e >60 anos). Ocorre quando a proliferação linfóide clonal afeta os pulmões e não apresenta disseminação extrapulmonar no momento do diagnóstico ou nos 3 meses seguintes. Conclusão: O tratamento é variável na literatura, devido a falta de diretrizes, e é determinado de acordo com a extensão da patologia. A abordagem pode ser via cirurgia, radioterapia ou quimioterapia, sendo que muitas vezes é realizada uma associação dos métodos

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Changes in Group B <i>Streptococcus</i> Colonization among Pregnant Women before and after the Onset of the COVID-19 Pandemic in Brazil

Group B Streptococcus (GBS) is a leading cause of neonatal infections. The genitourinary and gastrointestinal tract of pregnant women are the main source of transmission to newborns. This work investigated the prevalence and characterized GBS from pregnant women in Rio de Janeiro, Brazil, comparing the periods before (January 2019 to March 2020; 521) and during (May 2020 to March 2021; 285) the COVID-19 pandemic. GBS was detected in 10.8% of anovaginal samples. Considering scenarios before and during the pandemic, GBS colonization rate significantly decreased (13.8% vs. 5.3%; p = 0.0001). No clinical and sociodemographic aspect was associated with GBS carriage (p > 0.05). A total of 80%, 13.8% and 4.6% GBS strains were non-susceptible to tetracycline, erythromycin and clindamycin, respectively. Serotype Ia was the most frequent (47.7%), followed by V (23.1%), II (18.4%), III (7.7%) and Ib (3.1%). An increasing trend of serotypes Ib and V, as well as of antimicrobial resistance rates, and a decreasing trend of serotypes II and III, were observed after the pandemic onset, albeit not statistically significant (p > 0.05). The reduction in GBS colonization rates and alterations in GBS serotypes and resistance profiles during the pandemic were not due to changes in the sociodemographic profile of the population. Considering that control and preventive measures related to the COVID-19 pandemic onset have impacted other infectious diseases, these results shed light on the need for the continuous surveillance of GBS among pregnant women in the post-pandemic era

ABC₂-SPH risk score for in-hospital mortality in COVID-19 patients

Objectives: The majority of available scores to assess mortality risk of coronavirus disease 2019 (COVID-19) patients in the emergency department have high risk of bias. Therefore, this cohort aimed to develop and validate a score at hospital admission for predicting in-hospital mortality in COVID-19 patients and to compare this score with other existing ones. Methods: Consecutive patients (≥ 18 years) with confirmed COVID-19 admitted to the participating hospitals were included. Logistic regression analysis was performed to develop a prediction model for in-hospital mortality, based on the 3978 patients admitted between March–July, 2020. The model was validated in the 1054 patients admitted during August–September, as well as in an external cohort of 474 Spanish patients. Results: Median (25–75th percentile) age of the model-derivation cohort was 60 (48–72) years, and in-hospital mortality was 20.3%. The validation cohorts had similar age distribution and in-hospital mortality. Seven significant variables were included in the risk score: age, blood urea nitrogen, number of comorbidities, C-reactive protein, SpO2/FiO2 ratio, platelet count, and heart rate. The model had high discriminatory value (AUROC 0.844, 95% CI 0.829–0.859), which was confirmed in the Brazilian (0.859 [95% CI 0.833–0.885]) and Spanish (0.894 [95% CI 0.870–0.919]) validation cohorts, and displayed better discrimination ability than other existing scores. It is implemented in a freely available online risk calculator (https://abc2sph.com/). Conclusions: An easy-to-use rapid scoring system based on characteristics of COVID-19 patients commonly available at hospital presentation was designed and validated for early stratification of in-hospital mortality risk of patients with COVID-19.</p