Human Coronaviruses SARS-CoV, MERS-CoV, and SARS-CoV-2 in Children

The novel coronavirus, known as 2019-nCoV or severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused an epidemic with high mortality and morbidity since December 2019, in Wuhan, China. The infection has now been transmitted to more than 210 countries worldwide and caused more than 200,000 deaths. Similar to other coronaviruses such as Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) and the Middle East Respiratory Syndrome Coronavirus (MERS-CoV), SARS-CoV-2 appears to less commonly affect pediatrics and to cause less severe disease along with fewer symptoms compared to adults. Available data suggest that the pediatric population is just as likely as adults to become infected with SARS-CoV-2. However, they may be asymptotic or have milder symptoms than adults; they can be potential carriers of the disease. This article reviews the present understanding of SARS-CoV-2 infection in the pediatric age group in comparison with MERS-CoV and SARS-CoV. (c) 2020 Elsevier Inc. All rights reserved

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

The Role of PIEZO2 in Human Mechanosensation

No Abstract

Vascular endothelial growth factor levels in tuberculosis: A systematic review and meta-analysis.

BackgroundChanges in endothelial function are implicated in the spread of tuberculosis (TB). Studies suggest a role for the vascular endothelial growth factor (VEGF) in TB-related endothelial function changes. However, the findings of studies investigating the VGEF profile in TB are not consistent, and no formal systematic review and meta-analysis exists summarizing these studies.MethodsWe did a meta-analysis of studies assessing VEGF levels in patients with TB. A systematic search on June 25, 2021, was conducted for eligible studies that made VEGF measurements in an unstimulated sample, e.g., a blood fraction (plasma or serum), cerebrospinal fluid (CSF), pleural effusion (PE), or bronchoalveolar lavage fluid, and ascites or pericardial fluid for patients with TB and controls without TB. Also, studies that made simultaneous measurements of VEGF in blood and PE or CSF in the same patients with TB were included. Longitudinal studies that provided these data at baseline or compared pre-post anti-tuberculosis treatment (ATT) levels of VEGF were included. The primary outcome was the standardized mean difference (SMD) of VEGF levels between the comparison groups.Results52 studies were included in the meta-analysis. There were 1787 patients with TB and 3352 control subjects of eight categories: 107 patients with transudative pleural effusion, 228 patients with congestive heart failure (CHF)/chronic renal failure (CRF), 261 patients with empyema and parapneumonic effusion (PPE), 241 patients with cirrhosis, 694 healthy controls (with latent TB infection or uninfected individuals), 20 patients with inactive tuberculous meningitis (TBM), 123 patients with non-TBM, and 1678 patients with malignancy. The main findings are as follows: (1) serum levels of VEGF are higher in patients with active TB compared with healthy controls without other respiratory diseases, including those with latent TB infection or uninfected individuals; (2) both serum and pleural levels of VEGF are increased in patients with TPE compared with patients with transudative, CHF/CRF, or cirrhotic pleural effusion; (3) ascitic/pericardial fluid, serum, and pleural levels of VEGF are decreased in patients with TB compared with patients with malignancy; (4) pleural levels of VEGF are lower in patients with TPE compared with those with empyema and PPE, whereas serum levels of VEGF are not different between these patients; (5) both CSF and serum levels of VEGF are increased in patients with active TBM compared with controls, including patients with inactive TBM or non-TBM subjects; (6) post-ATT levels of VEGF are increased compared with pre-ATT levels of VEGF; and (7) the mean age and male percentage of the TB group explained large and total amount of heterogeneity for the meta-analysis of blood and pleural VEGF levels compared with healthy controls and patients with PPE, respectively, whereas these moderators did not show any significant interaction with the effect size for other analyses.DiscussionThe important limitation of the study is that we could not address the high heterogeneity among studies. There might be unmeasured factors behind this heterogeneity that need to be explored in future research. Meta-analysis findings align with the hypothesis that TB may be associated with abnormal vascular function, and both local and systemic levels of VEGF can be used to trace this abnormality

Artificial Intelligence and Acute Appendicitis: A Systematic Review of Diagnostic and Prognostic Models

Abstract Background To assess the efficacy of artificial intelligence (AI) models in diagnosing and prognosticating acute appendicitis (AA) in adult patients compared to traditional methods. AA is a common cause of emergency department visits and abdominal surgeries. It is typically diagnosed through clinical assessments, laboratory tests, and imaging studies. However, traditional diagnostic methods can be time-consuming and inaccurate. Machine learning models have shown promise in improving diagnostic accuracy and predicting outcomes. Main body A systematic review following the PRISMA guidelines was conducted, searching PubMed, Embase, Scopus, and Web of Science databases. Studies were evaluated for risk of bias using the Prediction Model Risk of Bias Assessment Tool. Data points extracted included model type, input features, validation strategies, and key performance metrics. Results In total, 29 studies were analyzed, out of which 21 focused on diagnosis, seven on prognosis, and one on both. Artificial neural networks (ANNs) were the most commonly employed algorithm for diagnosis. Both ANN and logistic regression were also widely used for categorizing types of AA. ANNs showed high performance in most cases, with accuracy rates often exceeding 80% and AUC values peaking at 0.985. The models also demonstrated promising results in predicting postoperative outcomes such as sepsis risk and ICU admission. Risk of bias was identified in a majority of studies, with selection bias and lack of internal validation being the most common issues. Conclusion AI algorithms demonstrate significant promise in diagnosing and prognosticating AA, often surpassing traditional methods and clinical scores such as the Alvarado scoring system in terms of speed and accuracy

Deciphering variability in the role of interleukin-1β in Parkinson’s disease

Although the role of inflammation in neurodegeneration has been well acknowledged, less is known on the issue of each cytokine in specific neurodegenerative diseases. In this review, we will present evidence elucidating that interleukin-1β (IL-1β) has a multi-faceted character in pathogenesis of Parkinson’s disease, which is a progressive neurodegenerative disorder. Increased levels of IL-1β were found in PD patients. Besides, PD symptoms were observed in IL-1β wild-type, but not deficient, animals. These lines of evidence suggest that IL-1β may contribute to the initiation or progression of PD. On the other hand, some studies reported decreased levels of IL-1β in PD patients. Also, genetic studies provided evidence suggesting that IL-1β may protect individuals against PD. Presumably, the broad range of IL-1β role is due to its interaction with both upstream and downstream mediators. Differences in IL-1β levels could be because of glia population (i.e. microglia and astrocytes), mitogen-activated protein kinase and nuclear factor κ light-chain-enhancer of activated B cells signaling pathways, and several mediators (including cyclooxygenase, neurotrophic factors, reactive oxygen species, caspases, heme oxygenase-1, and matrix metalloproteinases). Although far from practice at this point, unraveling theoretical therapeutic targets based on the up-down IL-1β neuroweb could facilitate the development of strategies that are likely to be used for pharmaceutical designs of anti-neurodegenerative drugs of the future.Fil: Saghazadeh, Amene. Tehran University Of Medical Sciences; IránFil: Ferrari, Carina Cintia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Rezaei, Nima. Tehran University Of Medical Sciences; Irá

Clinical safety and efficacy of bispecific antibody in the treatment of solid tumors: A protocol for a systematic review

Background Cancers are among the most common causes of mortality and morbidity. Recently, bispecific antibodies (BsAbs) have been used for cancer treatment. The aim of this systematic review and meta-analysis will be to determine the safety and efficacy of BsAbs in the treatment of solid tumors. Methods We will search five electronic databases, PubMed, EMBASE, Scopus, Web of Science, and CENTRAL, in addition to Clinical-Trials.gov and metaRegister of controlled trials and backward and forward citation searching of included studies. Eligible studies will be controlled clinical trials evaluating safety and/or efficacy of BsAbs in adult patients with solid tumors. The primary outcomes will be the incidence of safety and efficacy measures. Title and/or abstract screening, full text reviewing, data collection, and quality assessment will be done by two reviewers. We will use The Cochrane Collaboration’s risk of bias tool 2 (RoB2) to assess the quality of included studies. If I-square heterogeneity was greater than 40%, we will implement random effect model. Subgroup analysis and meta-regression will be undertaken if applicable. The metaprop command of STATA will be used to calculate frequency of AEs. Funnel plot, Egger’s and Peter’s tests will be utilized to evaluate publication bias in case of including at least ten studies. We will use sensitivity analysis to evaluate the effects of funding sources and continuity correction on effects size. Conclusions The findings of the present study will provide information on safety and efficacy of BsAbs for physicians and researchers in the management of solid tumors. Trial registration Registration on PROSPERO CRD42021227879 Also, important protocol amendments will be stated on PROSPERO registration