Effects of curing modes on depth of cure and microtensile bond strength of bulk fill composites to dentin

Objectives: To compare the microtensile bond strength (µTBS) and depth of cure (DOC) of bulk-fill composites cured by monowave (MW) and polywave (PW) LED units using different curing times. Methodology: Three composites were tested: Tetric EvoCeram Bulk Fill (TBF), Filtek Bulk Fill (FBF), and Tetric EvoCeram (T; control). Flat dentin surfaces treated with adhesive (AdheSE Universal®, Ivoclar Vivadent) were bonded with 4 mm cylindrical samples of each bulk-fill composite material (n=6) and cured with monowave (Satelec) or polywave (Bluephase Style) curing units for 10 or 20 seconds. After 24 hours, teeth were sectioned into individual 0.9 mm2 beams and tested for µTBS. Failure modes were analysed. Moreover, the DOC scrape test (IOS 4090) was completed (n=5) following the same curing protocols. Two-way ANOVA (a=0.05) was performed, isolating light-curing units. Results: For samples cured with the MW light-curing unit, no significant effects were observed in the µTBS results between any of the resin composite brands and the curing times. Conversely, when resins were cured with a PW light unit, a significant effect was observed for TBF resin. In general, bulk-fill composites presented greater DOC and longer curing time resulted in higher DOC for all composites. Conclusion: The µTBS of the composites to dentin was not affected by the curing mode of the resins, except for TBF cured with PW light unit. Bulk-fill composites exhibit greater DOC than conventional resin-based composites

Investigating the health of resection margins for conducting intra operative radiotherapy during breast conservative surgery

To conduct radiotherapy during operation in treating the breast cancer through breast conservative surgery method, we must ensure the healthiness of resection margins during the operation. Then, electron ray is flashed to the surrounding tissues in order to reduce the chance of the presence of any microscopic cancer cells. The present research studies the ability of Frozen in diagnosing the health or involvement of margins during the operation and seeks to compare the results of Frozen with the results of gold standard (permanent pathology) diagnostic method in patients suffering from breast cancer who have undergone breast conservative surgery and Intra-operative radiotherapy. In this observational study conducted in the form of a retrospective research, some 496 patients suffering from breast cancer who have undergone breast conservative surgery were selected through the convenience sampling method and the results of breast margin pathologies conducted through Permanent and Frozen section were compared against one another so that we may find the sensitivities and specifications of the Frozen sections in comparison with the Permanent method. In the Frozen and Permanent methods, the margins had a freedom degree of 83.1 and 98 respectively. The sensitivity and peculiarity of the Frozen section as compared to the Permanent method were 80 and 84 respectively, however the negative predictive value was 99.5. A significant relationship was observed here based on chi square test. The level of accuracy was 84.3, and the pseudo negative weas 0.5. The compliance level of the Permanent and Frozen diagnostic methods was 84.3. It is therefore concluded that the Frozen section has a good degree of compliance in determining the state of margin of the removed cancerous lump in breast conservative surgeries and false negative reports have been issued for 0.5 of the cases. Thus, it is recommended to use the Frozen section before radiotherapy to determine the freedom of margins. � 2016, Oriental Scientific Publishing Company. All rights reserved

Internet of Things 2.0: Concepts, Applications, and Future Directions

Applications and technologies of the Internet of Things are in high demand with the increase of network devices. With the development of technologies such as 5G, machine learning, edge computing, and Industry 4.0, the Internet of Things has evolved. This survey article discusses the evolution of the Internet of Things and presents the vision for Internet of Things 2.0. The Internet of Things 2.0 development is discussed across seven major fields. These fields are machine learning intelligence, mission critical communication, scalability, energy harvesting-based energy sustainability, interoperability, user friendly IoT, and security. Other than these major fields, the architectural development of the Internet of Things and major types of applications are also reviewed. Finally, this article ends with the vision and current limitations of the Internet of Things in future network environments

Transmission and evolution of the Middle East respiratory syndrome coronavirus in Saudi Arabia:a descriptive genomic study

BACKGROUND: Since June, 2012, Middle East respiratory syndrome coronavirus (MERS-CoV) has, worldwide, caused 104 infections in people including 49 deaths, with 82 cases and 41 deaths reported from Saudi Arabia. In addition to confirming diagnosis, we generated the MERS-CoV genomic sequences obtained directly from patient samples to provide important information on MERS-CoV transmission, evolution, and origin. METHODS: Full genome deep sequencing was done on nucleic acid extracted directly from PCR-confirmed clinical samples. Viral genomes were obtained from 21 MERS cases of which 13 had 100%, four 85-95%, and four 30-50% genome coverage. Phylogenetic analysis of the 21 sequences, combined with nine published MERS-CoV genomes, was done. FINDINGS: Three distinct MERS-CoV genotypes were identified in Riyadh. Phylogeographic analyses suggest the MERS-CoV zoonotic reservoir is geographically disperse. Selection analysis of the MERS-CoV genomes reveals the expected accumulation of genetic diversity including changes in the S protein. The genetic diversity in the Al-Hasa cluster suggests that the hospital outbreak might have had more than one virus introduction. INTERPRETATION: We present the largest number of MERS-CoV genomes (21) described so far. MERS-CoV full genome sequences provide greater detail in tracking transmission. Multiple introductions of MERS-CoV are identified and suggest lower R0 values. Transmission within Saudi Arabia is consistent with either movement of an animal reservoir, animal products, or movement of infected people. Further definition of the exposures responsible for the sporadic introductions of MERS-CoV into human populations is urgently needed. FUNDING: Saudi Arabian Ministry of Health, Wellcome Trust, European Community, and National Institute of Health Research University College London Hospitals Biomedical Research Centre

Biallelic variants in PCDHGC4 cause a novel neurodevelopmental syndrome with progressive microcephaly, seizures, and joint anomalies.

PURPOSE: We aimed to define a novel autosomal recessive neurodevelopmental disorder, characterize its clinical features, and identify the underlying genetic cause for this condition. METHODS: We performed a detailed clinical characterization of 19 individuals from nine unrelated, consanguineous families with a neurodevelopmental disorder. We used genome/exome sequencing approaches, linkage and cosegregation analyses to identify disease-causing variants, and we performed three-dimensional molecular in silico analysis to predict causality of variants where applicable. RESULTS: In all affected individuals who presented with a neurodevelopmental syndrome with progressive microcephaly, seizures, and intellectual disability we identified biallelic disease-causing variants in Protocadherin-gamma-C4 (PCDHGC4). Five variants were predicted to induce premature protein truncation leading to a loss of PCDHGC4 function. The three detected missense variants were located in extracellular cadherin (EC) domains EC5 and EC6 of PCDHGC4, and in silico analysis of the affected residues showed that two of these substitutions were predicted to influence the Ca2+-binding affinity, which is essential for multimerization of the protein, whereas the third missense variant directly influenced the cis-dimerization interface of PCDHGC4. CONCLUSION: We show that biallelic variants in PCDHGC4 are causing a novel autosomal recessive neurodevelopmental disorder and link PCDHGC4 as a member of the clustered PCDH family to a Mendelian disorder in humans

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

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