A systematic review on COVID-19 pandemic with special emphasis on curative potentials of Nigeria based medicinal plants

Despite the frightening mortality rate associated with COVID-19, there is no known approved drug to effectively combat the pandemic. COVID-19 clinical manifestations include fever, fatigue, cough, shortness of breath, and other complications. At present, there is no known effective treatment or vaccine that can mitigate/inhibit SARS-CoV-2. Available clinical intervention for COVID-19 is only palliative and limited to support. Thus, there is an exigent need for effective and non-invasive treatment. This article evaluates the possible mechanism of actions of SARS-CoV-2 and present Nigeria based medicinal plants which have pharmacological and biological activities that can mitigate the hallmarks of the pathogenesis of COVID-19. SARS-CoV-2 mode of actions includes hyper-inflammation characterized by a severe and fatal hyper-cytokinaemia with multi-organ failure; immunosuppression; reduction of angiotensin-converting enzyme 2 (ACE2) to enhance pulmonary vascular permeability causing damage to the alveoli; and further activated by open reading frame (ORF)3a, ORF3b, and ORF7a via c-Jun N- terminal kinase (JNK) pathway which induces lung damage. These mechanisms of action of SARS-CoV-2 can be mitigated by a combination therapy of medicinal herbs based on their pharmacological activities. Since the clinical manifestations of COVID-19 are multifactorial with co-morbidities, we strongly recommend the use of combined therapy such that two or more herbs with specific therapeutic actions are administered to combat the mediators of the disease.publishedVersionFil: Oladele, Johnson O. Kings University; Nigeria.Fil: Ajayi, Ebenezer Idowu O. Osun State University; Nigeria.Fil: Ajayi, Ebenezer Idowu O. Universidad Nacional de Córdoba. Rectorado; Argentina.Fil: Ajayi, Ebenezer Idowu O. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigación Médica Mercedes y Martín Ferreyra.; Argentina.Fil: Oyeleke, Oyedotun M. Kings University; Nigeria.Fil: Oladele, Oluwaseun T. Osun State University; Nigeria.Fil: Olowookere, Boyede D. Kings University; Nigeria.Fil: Adeniyi, Boluwaji M. Benue State University; Nigeria.Fil: Oyewole, Olu I. Osun State University; Nigeria.Fil: Oladiji, Adenike T. University of Ilorin; Nigeria

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

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

Using machine learning for prediction of spray coated perovskite solar cells efficiency: From experimental to theoretical models

Low-cost perovskite solar cells (PSCs) have experienced unprecedented gains in power conversion efficiency (PCE) of up to 25% of lab-scale devices. To be realized in the market, however, PSCs are not only required to be efficient but also scalable in production. While spray coating has viability as an industrial manufacturing process for perovskite photovoltaics scaling, optimizing the spray conditions is often seen as a challenging and time-consuming process due to its complex and multidimensional parameters. Herein, we use a machine learning (ML) approach to capture the relationship between spray parameter settings to the resultant photoconversion efficiency (PCE) of PSCs from experimental collected data points. This data-driven approach has the potential to accurately predict PCE values given the manufacturing parameters, enabling optimization and resulting in an increased experimentally recorded PCE. Furthermore, we also used a Convolutional Neural Network (CNN) to predict defect size distributions in the PSC structures to improve the understanding of defect formation mechanism at given spray parameters. The implications of the results are discussed for optimizing spray manufacturing process of efficient perovskite photovoltaics

Corrosion behavior of 5-hydroxytryptophan (HTP)/epoxy and clay particle-reinforced epoxy composite steel coatings

The corrosion behavior of 5-hydroxytryptophan (HTP), and clay particulate reinforced epoxy coatings is studied on a steel substrate that is used widely in pipelines and tanks. The corrosion behavior was studied in sodium chloride (3.5 wt. % NaCl) solutions that simulate potential seawater exposure at pH 3 and 7. X-ray diffraction (XRD) and Scanning Electron Microscopy (SEM) were used for microstructural characterization of the samples. The thermal stability was characterized using Thermogravimetric Analysis (TGA). The underlying corrosion reactions and reaction products were also elucidated via Fourier Transform Infrared Spectroscopy (FTIR). Electrochemical impedance spectroscopy (EIS) and in-situ observations of interfacial blisters were used to study the underlying degradation mechanisms. Electrochemical impedance spectroscopy revealed that for prolonged exposure of about 90 days and above, the composite materials exhibited better corrosion resistance at a pH of 3 as seen by the higher diameter of the Nyquist plot. Fewer corrosion products were observed on the scribed areas of the HTP samples in the scribe test in pH of 3 corroding environment. This signifies improved adhesion of the coatings in that environment for the HTP/epoxy coatings. The results obtained also show that a 1 mm blister size was observed in the pristine epoxy sample while no blisters were observed in the clay/epoxy and HTP/epoxy samples exposed at pH of 3. In the pH 7 environment, the EIS experiment revealed the presence of blisters with diameters in the range of 1–4 mm, after exposure for 90 days. The implications of the results are discussed for the corrosion protection of steel surfaces with composite coatings

Effects of substrates on the performance of optoelectronic devices: A review

This review discusses the effects of substrates on devices fabricated for optoelectronic applications. It includes the types and characteristics of substrates, synthesis and fabrication of substrates, and the influence of substrates on the optical properties, surface morphology and current-voltage behaviour of optoelectronic devices. The study showed that two main types of substrates: planar and textured are commonly used in the industry. Flexibility, semi-rigidity and rigidity are characteristics of the substrates and they vary in modulus, transparency and texture. Whereas glass and metal substrates can be produced via melt casting, polydimethylsiloxane (PDMS), polyethylene terephthalate (PET), etc are produced by crosslinking polymer base materials with curing agents. The mechanical and current-voltage characteristics are also shown for planar and textured substrate-based devices. The textured substrates showed ridges, wrinkles, buckled surface morphology whereas the planar showed uniform and largely flat morphology. Textured substrates also recorded higher optical absorbance and improved device efficiencies compared with planar substrates. The molecular configuration of the polymer chains are edged-on for planar substrates and both edge-on and face-on for textured substrates. The findings and their implications have been discussed to highlight the importance of substrates in the fabrication and performance of optoelectronic devices