Potential Role of Plant Growth Regulators in Administering Crucial Processes Against Abiotic Stresses

Plant growth regulators are naturally biosynthesized chemicals in plants that influence physiological processes. Their synthetic analogous trigger numerous biochemical and physiological processes involved in the growth and development of plants. Nowadays, due to changing climatic scenario, numerous biotic and abiotic stresses hamper seed germination, seedling growth, and plant development leading to a decline in biological and economic yields. However, plant growth regulators (PGRs) can potentially play a fundamental role in regulating plant responses to various abiotic stresses and hence, contribute to plant adaptation under adverse environments. The major effects of abiotic stresses are growth and yield disturbance, and both these effects are directly overseen by the PGRs. Different types of PGRs such as abscisic acid (ABA), salicylic acid (SA), ethylene (ET), and jasmonates (JAs) are connected to boosting the response of plants to multiple stresses. In contrast, PGRs including cytokinins (CKs), gibberellins (GAs), auxin, and relatively novel PGRs such as strigolactones (SLs), and brassinosteroids (BRs) are involved in plant growth and development under normal and stressful environmental conditions. Besides, polyamines and nitric oxide (NO), although not considered as phytohormones, have been included in the current review due to their involvement in the regulation of several plant processes and stress responses. These PGRs are crucial for regulating stress adaptation through the modulates physiological, biochemical, and molecular processes and activation of the defense system, upregulating of transcript levels, transcription factors, metabolism genes, and stress proteins at cellular levels. The current review presents an acumen of the recent progress made on different PGRs to improve plant tolerance to abiotic stress such as heat, drought, salinity, and flood. Moreover, it highlights the research gaps on underlying mechanisms of PGRs biosynthesis under stressed conditions and their potential roles in imparting tolerance against adverse effects of suboptimal growth conditions.Fil: Sabagh, Ayman EL. Kafrelsheikh University; EgiptoFil: Mbarki, Sonia. National Institute Of Research In Rural Engineering; TúnezFil: Hossain, Akbar. Bangladesh Agricultural Research Institute; BangladeshFil: Iqbal, Muhammad Aamir. University Of Poonch Rawalakot; PakistánFil: Islam, Mohammad Sohidul. Hajee Mohammad Danesh And Technology University; BangladeshFil: Raza, Ali. Fujian Agriculture And Forestry University; ChinaFil: Llanes, Analia Susana. Universidad Nacional de Rio Cuarto. Facultad de Cs.exactas Fisicoquimicas y Naturales. Instituto de Investigaciones Agrobiotecnologicas. - Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Cordoba. Instituto de Investigaciones Agrobiotecnologicas.; ArgentinaFil: Reginato, Mariana Andrea. Universidad Nacional de Rio Cuarto. Facultad de Cs.exactas Fisicoquimicas y Naturales. Instituto de Investigaciones Agrobiotecnologicas. - Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Cordoba. Instituto de Investigaciones Agrobiotecnologicas.; ArgentinaFil: Rahman, Md Atikur. Grassland And Forage Division National Institute; Corea del SurFil: Mahboob, Wajid. Nuclear Institute Of Agriculture,; PakistánFil: Singhal, Rajesh Kumar. Indian Council Of Agricultural Research; IndiaFil: Kumari, Arpna. Guru Nanak Dev University; IndiaFil: Rajendran, Arvind. Vellore Institute Of Technology; IndiaFil: Wasaya, Allah. Bahauddin Zakariya University; PakistánFil: Javed, Talha. Fujian Agriculture And Forestry University; JapónFil: Shabbir, Rubab. University Of Poonch Rawalakot; PakistánFil: Rahim, Junaid. University Of Çukurova; PakistánFil: Barutçular, Celaleddin. Institute Of Crop Science And Resource Conservation; AlemaniaFil: Habib Ur Rahman, Muhammad. Sichuan Agricultural University; ChinaFil: Raza, Muhammad Ali. Sichuan Agricultural University; ChinaFil: Ratnasekera, Disna. University Of Ruhuna; Sri LankaFil: Konuskan l, Ömer. Mustafa Kemal University; TurquíaFil: Hossain, Mohammad Anwar. Bangladesh Agricultural Research Institute; BangladeshFil: Meena, Vijay Singh. Indian Council Of Agricultural Research; IndiaFil: Ahmed, Sharif. Bangladesh Agricultural Research Institute; BangladeshFil: Ahmad, Zahoor. Bangladesh Wheat And Maize Research Institute; BangladeshFil: Mubeen, Muhammad. Sichuan Agricultural University; ChinaFil: Singh, Kulvir. Punjab Agricultural University; IndiaFil: Skalicky, Milan. Czech University Of Life Sciences Prague; República ChecaFil: Brestic, Marian. Slovak University Of Agriculture; EslovaquiaFil: Sytar, Oksana. Slovak University Of Agriculture; EsloveniaFil: Karademir, Emine. Siirt University; TurquíaFil: Karademir, Cetin. Siirt University; TurquíaFil: Erman, Murat. Siirt University; TurquíaFil: Farooq, Muhammad. College Of Agricultural And Marine Sciences Sultan; Omá

Spatial, temporal, and demographic patterns in prevalence of smoking tobacco use and attributable disease burden in 204 countries and territories, 1990-2019 : a systematic analysis from the Global Burden of Disease Study 2019

Background Ending the global tobacco epidemic is a defining challenge in global health. Timely and comprehensive estimates of the prevalence of smoking tobacco use and attributable disease burden are needed to guide tobacco control efforts nationally and globally. Methods We estimated the prevalence of smoking tobacco use and attributable disease burden for 204 countries and territories, by age and sex, from 1990 to 2019 as part of the Global Burden of Diseases, Injuries, and Risk Factors Study. We modelled multiple smoking-related indicators from 3625 nationally representative surveys. We completed systematic reviews and did Bayesian meta-regressions for 36 causally linked health outcomes to estimate non-linear dose-response risk curves for current and former smokers. We used a direct estimation approach to estimate attributable burden, providing more comprehensive estimates of the health effects of smoking than previously available. Findings Globally in 2019, 1.14 billion (95% uncertainty interval 1.13-1.16) individuals were current smokers, who consumed 7.41 trillion (7.11-7.74) cigarette-equivalents of tobacco in 2019. Although prevalence of smoking had decreased significantly since 1990 among both males (27.5% [26. 5-28.5] reduction) and females (37.7% [35.4-39.9] reduction) aged 15 years and older, population growth has led to a significant increase in the total number of smokers from 0.99 billion (0.98-1.00) in 1990. Globally in 2019, smoking tobacco use accounted for 7.69 million (7.16-8.20) deaths and 200 million (185-214) disability-adjusted life-years, and was the leading risk factor for death among males (20.2% [19.3-21.1] of male deaths). 6.68 million [86.9%] of 7.69 million deaths attributable to smoking tobacco use were among current smokers. Interpretation In the absence of intervention, the annual toll of 7.69 million deaths and 200 million disability-adjusted life-years attributable to smoking will increase over the coming decades. Substantial progress in reducing the prevalence of smoking tobacco use has been observed in countries from all regions and at all stages of development, but a large implementation gap remains for tobacco control. Countries have a dear and urgent opportunity to pass strong, evidence-based policies to accelerate reductions in the prevalence of smoking and reap massive health benefits for their citizens. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd.Peer reviewe

Spatial, temporal, and demographic patterns in prevalence of chewing tobacco use in 204 countries and territories, 1990-2019 : a systematic analysis from the Global Burden of Disease Study 2019

Interpretation Chewing tobacco remains a substantial public health problem in several regions of the world, and predominantly in south Asia. We found little change in the prevalence of chewing tobacco use between 1990 and 2019, and that control efforts have had much larger effects on the prevalence of smoking tobacco use than on chewing tobacco use in some countries. Mitigating the health effects of chewing tobacco requires stronger regulations and policies that specifically target use of chewing tobacco, especially in countries with high prevalence. Findings In 2019, 273 center dot 9 million (95% uncertainty interval 258 center dot 5 to 290 center dot 9) people aged 15 years and older used chewing tobacco, and the global age-standardised prevalence of chewing tobacco use was 4 center dot 72% (4 center dot 46 to 5 center dot 01). 228 center dot 2 million (213 center dot 6 to 244 center dot 7; 83 center dot 29% [82 center dot 15 to 84 center dot 42]) chewing tobacco users lived in the south Asia region. Prevalence among young people aged 15-19 years was over 10% in seven locations in 2019. Although global agestandardised prevalence of smoking tobacco use decreased significantly between 1990 and 2019 (annualised rate of change: -1 center dot 21% [-1 center dot 26 to -1 center dot 16]), similar progress was not observed for chewing tobacco (0 center dot 46% [0 center dot 13 to 0 center dot 79]). Among the 12 highest prevalence countries (Bangladesh, Bhutan, Cambodia, India, Madagascar, Marshall Islands, Myanmar, Nepal, Pakistan, Palau, Sri Lanka, and Yemen), only Yemen had a significant decrease in the prevalence of chewing tobacco use, which was among males between 1990 and 2019 (-0 center dot 94% [-1 center dot 72 to -0 center dot 14]), compared with nine of 12 countries that had significant decreases in the prevalence of smoking tobacco. Among females, none of these 12 countries had significant decreases in prevalence of chewing tobacco use, whereas seven of 12 countries had a significant decrease in the prevalence of tobacco smoking use for the period. Summary Background Chewing tobacco and other types of smokeless tobacco use have had less attention from the global health community than smoked tobacco use. However, the practice is popular in many parts of the world and has been linked to several adverse health outcomes. Understanding trends in prevalence with age, over time, and by location and sex is important for policy setting and in relation to monitoring and assessing commitment to the WHO Framework Convention on Tobacco Control. Methods We estimated prevalence of chewing tobacco use as part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2019 using a modelling strategy that used information on multiple types of smokeless tobacco products. We generated a time series of prevalence of chewing tobacco use among individuals aged 15 years and older from 1990 to 2019 in 204 countries and territories, including age-sex specific estimates. We also compared these trends to those of smoked tobacco over the same time period. Findings In 2019, 273 & middot;9 million (95% uncertainty interval 258 & middot;5 to 290 & middot;9) people aged 15 years and older used chewing tobacco, and the global age-standardised prevalence of chewing tobacco use was 4 & middot;72% (4 & middot;46 to 5 & middot;01). 228 & middot;2 million (213 & middot;6 to 244 & middot;7; 83 & middot;29% [82 & middot;15 to 84 & middot;42]) chewing tobacco users lived in the south Asia region. Prevalence among young people aged 15-19 years was over 10% in seven locations in 2019. Although global age standardised prevalence of smoking tobacco use decreased significantly between 1990 and 2019 (annualised rate of change: -1 & middot;21% [-1 & middot;26 to -1 & middot;16]), similar progress was not observed for chewing tobacco (0 & middot;46% [0 & middot;13 to 0 & middot;79]). Among the 12 highest prevalence countries (Bangladesh, Bhutan, Cambodia, India, Madagascar, Marshall Islands, Myanmar, Nepal, Pakistan, Palau, Sri Lanka, and Yemen), only Yemen had a significant decrease in the prevalence of chewing tobacco use, which was among males between 1990 and 2019 (-0 & middot;94% [-1 & middot;72 to -0 & middot;14]), compared with nine of 12 countries that had significant decreases in the prevalence of smoking tobacco. Among females, none of these 12 countries had significant decreases in prevalence of chewing tobacco use, whereas seven of 12 countries had a significant decrease in the prevalence of tobacco smoking use for the period. Interpretation Chewing tobacco remains a substantial public health problem in several regions of the world, and predominantly in south Asia. We found little change in the prevalence of chewing tobacco use between 1990 and 2019, and that control efforts have had much larger effects on the prevalence of smoking tobacco use than on chewing tobacco use in some countries. Mitigating the health effects of chewing tobacco requires stronger regulations and policies that specifically target use of chewing tobacco, especially in countries with high prevalence. Copyright (c) 2021 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

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Forecasting of Maize Production in Bangladesh Using Time Series Data

Maize has been gaining importance as one of the major grain crops in Bangladesh in recent years. Due to its multiple uses, i.e., food, feed and other industrial uses, maize production and its possible trend have created great interest among the policy planners. This study aims to forecast future production of maize in Bangladesh using both Box-Jenkins autoregressive integrated moving average (ARIMA) and mixed model approach (dynamic regression model) using secondary yearly data, for the growing seasons 1970-71 to 2019-20, published by the Bangladesh Bureau of Statistics (BBS). Our analyses suggest that ARIMA (0, 2, 1) is the best model for forecasting maize production all over Bangladesh. However, when the area of maize is considered the mixed model with ARIMA (1, 0, 0) performs better than the univariate ARIMA (0, 2, 1) model. The length of the 95% confidence interval of the forecast values of the mixed model is smaller than that of the ARIMA model indicating its better predictive performance. These forecast values will be useful for planning resources and making appropriate decisions regarding imports and exports by the government before harvesting

Forecasting of Maize Production in Bangladesh Using Time Series Data

Maize has been gaining importance as one of the major grain crops in Bangladesh in recent years. Due to its multiple uses, i.e., food, feed and other industrial uses, maize production and its possible trend have created great interest among the policy planners. This study aims to forecast future production of maize in Bangladesh using both Box-Jenkins autoregressive integrated moving average (ARIMA) and mixed model approach (dynamic regression model) using secondary yearly data, for the growing seasons 1970-71 to 2019-20, published by the Bangladesh Bureau of Statistics (BBS). Our analyses suggest that ARIMA (0, 2, 1) is the best model for forecasting maize production all over Bangladesh. However, when the area of maize is considered the mixed model with ARIMA (1, 0, 0) performs better than the univariate ARIMA (0, 2, 1) model. The length of the 95% confidence interval of the forecast values of the mixed model is smaller than that of the ARIMA model indicating its better predictive performance. These forecast values will be useful for planning resources and making appropriate decisions regarding imports and exports by the government before harvesting