King Khalid University

Heavy metals are well-known environmental pollutants owing to their toxicity, longevity in the atmosphere, and ability to accumulate in the human body via bioaccumulation. The pollution of terrestrial and aquatic ecosystems with toxic heavy metals is a major environmental concern that has consequences for public health. Most heavy metals occur naturally, but a few are derived from anthropogenic sources. Heavy metals are characterized by their high atomic mass and toxicity to living organisms. Most heavy metals cause environmental and atmospheric pollution, and may be lethal to humans. Heavy metals can become strongly toxic by mixing with different environmental elements, such as water, soil, and air, and humans and other living organisms can be exposed to them through the food chain. Plenty of experimental studies were performed to appraise the promising treatment options from natural products. Additionally, nanotechnology based treatment options are being constantly developed. As an emerging field, nanotechnology is making substantial advances in the analysis and removal of heavy metals from complicated matrices. Removal of heavy metal has been accomplished by the use of a variety of nanomaterials, including graphene and its derivatives, magnetic nanoparticles, metal oxide nanoparticles, and carbon nanotubes, to name a few. Using nanotechnology for heavy metal analysis and removal from food and water resources provides many benefits over traditional methods. These advantages include a broad linear range, low detection and quantification limits, a high sensitivity, and high selectivity. Therefore this review aimed to explore the environmental consequences of the heavy metals, toxicity to the human health, as well as novel therapeutics development from the natural resources. Additionally, nanotechnological and nanomedicinal applications to treat heavy metal toxicity are also highlighted in this review.

BACKGROUND: Misconceptions about ADHD stigmatize affected people, reduce credibility of providers, and prevent/delay treatment. To challenge misconceptions, we curated findings with strong evidence base. METHODS: We reviewed studies with more than 2000 participants or meta-analyses from five or more studies or 2000 or more participants. We excluded meta-analyses that did not assess publication bias, except for meta-analyses of prevalence. For network meta-analyses we required comparison adjusted funnel plots. We excluded treatment studies with waiting-list or treatment as usual controls. From this literature, we extracted evidence-based assertions about the disorder. RESULTS: We generated 208 empirically supported statements about ADHD. The status of the included statements as empirically supported is approved by 80 authors from 27 countries and 6 continents. The contents of the manuscript are endorsed by 366 people who have read this document and agree with its contents. CONCLUSIONS: Many findings in ADHD are supported by meta-analysis. These allow for firm statements about the nature, course, outcome causes, and treatments for disorders that are useful for reducing misconceptions and stigma.

Abstract The rapid advances in the internet and communication fields have resulted in a huge increase in the network size and the corresponding data. As a result, many novel attacks are being generated and have posed challenges for network security to accurately detect intrusions. Furthermore, the presence of the intruders with the aim to launch various attacks within the network cannot be ignored. An intrusion detection system (IDS) is one such tool that prevents the network from possible intrusions by inspecting the network traffic, to ensure its confidentiality, integrity, and availability. Despite enormous efforts by the researchers, IDS still faces challenges in improving detection accuracy while reducing false alarm rates and in detecting novel intrusions. Recently, machine learning (ML) and deep learning (DL)‐based IDS systems are being deployed as potential solutions to detect intrusions across the network in an efficient manner. This article first clarifies the concept of IDS and then provides the taxonomy based on the notable ML and DL techniques adopted in designing network‐based IDS (NIDS) systems. A comprehensive review of the recent NIDS‐based articles is provided by discussing the strengths and limitations of the proposed solutions. Then, recent trends and advancements of ML and DL‐based NIDS are provided in terms of the proposed methodology, evaluation metrics, and dataset selection. Using the shortcomings of the proposed methods, we highlighted various research challenges and provided the future scope for the research in improving ML and DL‐based NIDS.

Abstract Metal nanoparticles (MNPs) and metal oxide nanoparticles (MONPs) are used in numerous fields. The new nano-based entities are being strongly generated and incorporated into everyday personal care products, cosmetics, medicines, drug delivery, and clothing to impact industrial and manufacturing sectors, which means that nanomaterials commercialization and nano-assisted device will continuously grow. They can be prepared by many methods such as green synthesis and the conventional chemical synthesis methods. Green synthesis includes infinite accession to produce MNPs and MONPs with demanding properties. The structure–function relationships between nanomaterials and key information for life cycle evaluation lead to the production of high execution nanoscale materials that are gentle and environmentally friendly. Majority of plants have features as sustainable and renewable suppliers compared with microbes and enzymes, as they have the ability to pick up almost 75% of the light energy and transform it into chemical energy, contain chemicals like antioxidants and sugars, and play fundamental roles in the manufacture of nanoparticles. Plants considered the main factory for the green synthesis of MNPs and MONPs, and until now, different plant species have been used to study this, but the determined conditions should be taken into consideration to execute this preparation. In this study, we focus on the biosynthesis procedures to synthesize MNPs and MONPs, including comparison between green synthesis and the classical chemistry methods as well as the several new orientation of green synthesis of nanoparticles from different plant parts, especially plant leaf extracts. Plants with reducing compounds is the preferred choice for the synthesis of noble metals – metal ions can be reduced to the corresponding metals in the absence of any other chemicals under microwave irradiation conditions using benign solvent, water. Noble metals such as gold (Au), silver (Ag), platinum (Pt), and palladium (Pd) and other metals such as copper (Cu) and nickel (Ni), which are characterized by their optical, electronic, mechanical, magnetic, and chemical properties, leading to different technological applications. Plants with numerous reducing agents are suitable candidates for the manufacture of noble MNPs. The main purpose of this research is to give a background on green nanotechnology prospective evolution, pertinent concerns appeared related to the green synthesis of metal and metal oxide from plant extracts, nanoparticle formation mechanism, and the importance of flavonoids, vitamin B 2 , ascorbic acid (vitamin C), and phenolic compounds in the MNP and MONP production. The traditional sorghum beers are produced in many countries in Africa, but diversity in the production process may depend on the geographic localization. These beers are very rich in calories; B-group vitamins including thiamine, folic acid, riboflavin, and nicotinic acid; and essential amino acids such as lysine. However, the Western beers are more attractive than the traditional sorghum beers. The traditional sorghum beers have poor hygienic quality, organoleptic variations, and shorter shelf life compared with the Western beers. Many research studies on traditional sorghum beers have been carried out and documented in several African countries, especially the microbiological and biochemical properties, the technologies used in the manufacture processes, and synthetic characteristics of African traditional sorghum beers (ikigage, merissa, doro, dolo, pito, amgba, and tchoukoutou). The excellent resources for the production of greener biomaterials are plants and considerable advances have been achieved in many fields such as biotechnology and gene transfer. The manufactured biological nanomaterials have a great application in the pharmaceutical industry such as novel pharmaceuticals preparation, drug delivery personification procedures, and production of functional nanodevices.

Inflammation is a natural protective mechanism that occurs when the body's tissue homeostatic mechanisms are disrupted by biotic, physical, or chemical agents. The immune response generates pro-inflammatory mediators, but excessive output, such as chronic inflammation, contributes to many persistent diseases. Some phenolic compounds work in tandem with nonsteroidal anti-inflammatory drugs (NSAIDs) to inhibit pro-inflammatory mediators' activity or gene expression, including cyclooxygenase (COX). Various phenolic compounds can also act on transcription factors, such as nuclear factor-κB (NF-κB) or nuclear factor-erythroid factor 2-related factor 2 (Nrf-2), to up-or downregulate elements within the antioxidant response pathways. Phenolic compounds can inhibit enzymes associated with the development of human diseases and have been used to treat various common human ailments, including hypertension, metabolic problems, incendiary infections, and neurodegenerative diseases. The inhibition of the angiotensin-converting enzyme (ACE) by phenolic compounds has been used to treat hypertension. The inhibition of carbohydrate hydrolyzing enzyme represents a type 2 diabetes mellitus therapy, and cholinesterase inhibition has been applied to treat Alzheimer's disease (AD). Phenolic compounds have also demonstrated anti-inflammatory properties to treat skin diseases, rheumatoid arthritis, and inflammatory bowel disease. Plant extracts and phenolic compounds exert protective effects against oxidative stress and inflammation caused by airborne particulate matter, in addition to a range of anti-inflammatory, anticancer, anti-aging, antibacterial, and antiviral activities. Dietary polyphenols have been used to prevent and treat allergy-related diseases. The chemical and biological contributions of phenolic compounds to cardiovascular disease have also been described. This review summarizes the recent progress delineating the multifunctional roles of phenolic compounds, including their anti-inflammatory properties and the molecular pathways through which they exert anti-inflammatory effects on metabolic disorders. This study also discusses current issues and potential prospects for the therapeutic application of phenolic compounds to various human diseases.

This review summarizes and elaborates the new findings in this research domain of the green synthesis of silver nanoparticles (AgNPs) using different plant extracts and their potential applications as antimicrobial agents covering the literature since 2015. While highlighting the recently used different plants for the synthesis of highly efficient antimicrobial green AgNPs, we aim to provide a systematic in-depth discussion on the possible influence of the phytochemicals and their concentrations in the plants extracts, extraction solvent, and extraction temperature, as well as reaction temperature, pH, reaction time, and concentration of precursor on the size, shape and stability of the produced AgNPs. Exhaustive details of the plausible mechanism of the interaction of AgNPs with the cell wall of microbes, leading to cell death, and high antimicrobial activities have also been elaborated. The shape and size-dependent antimicrobial activities of the biogenic AgNPs and the enhanced antimicrobial activities by synergetic interaction of AgNPs with known commercial antibiotic drugs have also been comprehensively detailed.

It has been suggested that differences in body size between consumer and resource species may have important implications for interaction strengths, population dynamics, and eventually food web structure, function, and evolution. Still, the general distribution of consumer-'resource body-size ratios in real ecosystems, and whether they vary systematically among habitats or broad taxonomic groups, is poorly understood. Using a unique global database on consumer and resource body sizes, we show that the mean body-size ratios of aquatic herbivorous and detritivorous consumers are several orders of magnitude larger than those of carnivorous predators. Carnivorous predator-prey body-size ratios vary across different habitats and predator and prey types (invertebrates, ectotherm, and endotherm vertebrates). Predator-prey body-size ratios are on average significantly higher (1) in freshwater habitats than in marine or terrestrial habitats, (2) for vertebrate than for invertebrate predators, and (3) for invertebrate than for ectotherm vertebrate prey. If recent studies that relate body-size ratios to interaction strengths are general, our results suggest that mean consumer-resource interaction strengths may vary systematically across different habitat categories and consumer types.

Seafood safety is a critical requirement for sustained global quantitative and qualitative development. In recent years, unintended poisons have damaged human health and food quality. Heavy metals (HMs) distribution, speciation, bioaccumulation, and toxicity evaluation in aquatic settings are at their peak. Safe ecosystems have a significant influence in the possible composition of safe aquaculture products, which serve as the foundation of every food web. HMs eventually impose a number of stresses on the living organisms, contributing to increased mortality. Therefore, this study reflects and explains the exposure of heavy metals to aquatic food as well as the resulting health risks to humans. A more in-depth review on the translocation processes of metal toxins into seafood is provided. Finally, for achieving stability in aquatic environments, management techniques, genetic engineering, and remediation are recommended.

BACKGROUND The global obesity epidemic has paralleled a decrease in semen quality. Yet, the association between obesity and sperm parameters remains controversial. The purpose of this report was to update the evidence on the association between BMI and sperm count through a systematic review with meta-analysis. METHODS A systematic review of available literature (with no language restriction) was performed to investigate the impact of BMI on sperm count. Relevant studies published until June 2012 were identified from a Pubmed and EMBASE search. We also included unpublished data (n = 717 men) obtained from the Infertility Center of Bondy, France. Abstracts of relevant articles were examined and studies that could be included in this review were retrieved. Authors of relevant studies for the meta-analysis were contacted by email and asked to provide standardized data. RESULTS A total of 21 studies were included in the meta-analysis, resulting in a sample of 13 077 men from the general population and attending fertility clinics. Data were stratified according to the total sperm count as normozoospermia, oligozoospermia and azoospermia. Standardized weighted mean differences in sperm concentration did not differ significantly across BMI categories. There was a J-shaped relationship between BMI categories and risk of oligozoospermia or azoospermia. Compared with men of normal weight, the odds ratio (95% confidence interval) for oligozoospermia or azoospermia was 1.15 (0.93-1.43) for underweight, 1.11 (1.01-1.21) for overweight, 1.28 (1.06-1.55) for obese and 2.04 (1.59-2.62) for morbidly obese men. CONCLUSIONS Overweight and obesity were associated with an increased prevalence of azoospermia or oligozoospermia. The main limitation of this report is that studied populations varied, with men recruited from both the general population and infertile couples. Whether weight normalization could improve sperm parameters should be evaluated further.

Propolis is a natural substance collected by honey bees from various plants such as, poplar, palm, pine, conifer secretions, gums, resins, mucilage and leaf buds. It is collected and brought very painstakingly by honey bees to be used for sealing cracks and crevices occurring in their hives. Originally, it as an antiseptic meant for preventing bee-hive from microbial infections along with preventing decomposition of intruders. Additionally, propolis has been used in folk medicine for centuries. The biological characteristics of propolis depend upon its chemical composition, plant sources, geographical zone and seasons. More than 300 compounds have been identified in propolis such as, phenolic compounds, aromatic acids, essential oils, waxes and amino acids. Many scientific articles are published every year in different international journals, and several groups of researchers have focused their attention on the chemical compounds and biological activity of propolis.

Over the past few decades, interest in theories and algorithms for face recognition has been growing rapidly. Video surveillance, criminal identification, building access control, and unmanned and autonomous vehicles are just a few examples of concrete applications that are gaining attraction among industries. Various techniques are being developed including local, holistic, and hybrid approaches, which provide a face image description using only a few face image features or the whole facial features. The main contribution of this survey is to review some well-known techniques for each approach and to give the taxonomy of their categories. In the paper, a detailed comparison between these techniques is exposed by listing the advantages and the disadvantages of their schemes in terms of robustness, accuracy, complexity, and discrimination. One interesting feature mentioned in the paper is about the database used for face recognition. An overview of the most commonly used databases, including those of supervised and unsupervised learning, is given. Numerical results of the most interesting techniques are given along with the context of experiments and challenges handled by these techniques. Finally, a solid discussion is given in the paper about future directions in terms of techniques to be used for face recognition.

OBJECTIVE: To review systematically the evidence of effectiveness of physical interventions to interrupt or reduce the spread of respiratory viruses. DATA SOURCES: Cochrane Library, Medline, OldMedline, Embase, and CINAHL, without restrictions on language or publication. Data selection Studies of any intervention to prevent the transmission of respiratory viruses (isolation, quarantine, social distancing, barriers, personal protection, and hygiene). A search of study designs included randomised trials, cohort, case-control, crossover, before and after, and time series studies. After scanning of the titles, abstracts and full text articles as a first filter, a standardised form was used to assess the eligibility of the remainder. Risk of bias of randomised studies was assessed for generation of the allocation sequence, allocation concealment, blinding, and follow-up. Non-randomised studies were assessed for the presence of potential confounders and classified as being at low, medium, or high risk of bias. DATA SYNTHESIS: 58 papers of 59 studies were included. The quality of the studies was poor for all four randomised controlled trials and most cluster randomised controlled trials; the observational studies were of mixed quality. Meta-analysis of six case-control studies suggested that physical measures are highly effective in preventing the spread of severe acute respiratory syndrome: handwashing more than 10 times daily (odds ratio 0.45, 95% confidence interval 0.36 to 0.57; number needed to treat=4, 95% confidence interval 3.65 to 5.52), wearing masks (0.32, 0.25 to 0.40; NNT=6, 4.54 to 8.03), wearing N95 masks (0.09, 0.03 to 0.30; NNT=3, 2.37 to 4.06), wearing gloves (0.43, 0.29 to 0.65; NNT=5, 4.15 to 15.41), wearing gowns (0.23, 0.14 to 0.37; NNT=5, 3.37 to 7.12), and handwashing, masks, gloves, and gowns combined (0.09, 0.02 to 0.35; NNT=3, 2.66 to 4.97). The combination was also effective in interrupting the spread of influenza within households. The highest quality cluster randomised trials suggested that spread of respiratory viruses can be prevented by hygienic measures in younger children and within households. Evidence that the more uncomfortable and expensive N95 masks were superior to simple surgical masks was limited, but they caused skin irritation. The incremental effect of adding virucidals or antiseptics to normal handwashing to reduce respiratory disease remains uncertain. Global measures, such as screening at entry ports, were not properly evaluated. Evidence was limited for social distancing being effective, especially if related to risk of exposure-that is, the higher the risk the longer the distancing period. CONCLUSION: Routine long term implementation of some of the measures to interrupt or reduce the spread of respiratory viruses might be difficult. However, many simple and low cost interventions reduce the transmission of epidemic respiratory viruses. More resources should be invested into studying which physical interventions are the most effective, flexible, and cost effective means of minimising the impact of acute respiratory tract infections.

Plants generally secrete secondary metabolites in response to stress. These secondary metabolites are very useful for humankind as they possess a wide range of therapeutic activities. Secondary metabolites produced by plants include alkaloids, flavonoids, terpenoids, and steroids. Flavonoids are one of the classes of secondary metabolites of plants found mainly in edible plant parts such as fruits, vegetables, stems, grains, and bark. They are synthesized by the phenylpropanoid pathway. Flavonoids possess antibacterial, antiviral, antioxidant, anti-inflammatory, antimutagenic, and anticarcinogenic properties. Due to their various therapeutic applications, various pharmaceutical companies have exploited different plants for the production of flavonoids. To overcome this situation, various biotechnological strategies have been incorporated to improve the production of different types of flavonoids. In this review, we have highlighted the various types of flavonoids, their biosynthesis, properties, and different strategies to enhance the production of flavonoids.

Pesticides are chemical constituents used to prevent or control pests, including insects, rodents, fungi, weeds, and other unwanted organisms. Despite their advantages in crop production and disease management, the use of pesticides poses significant hazards to the environment and public health. Pesticide elements have now perpetually entered our atmosphere and subsequently contaminated water, food, and soil, leading to health threats ranging from acute to chronic toxicities. Pesticides can cause acute toxicity if a high dose is inhaled, ingested, or comes into contact with the skin or eyes, while prolonged or recurrent exposure to pesticides leads to chronic toxicity. Pesticides produce different types of toxicity, for instance, neurotoxicity, mutagenicity, carcinogenicity, teratogenicity, and endocrine disruption. The toxicity of a pesticide formulation may depend on the specific active ingredient and the presence of synergistic or inert compounds that can enhance or modify its toxicity. Safety concerns are the need of the hour to control contemporary pesticide-induced health hazards. The effectiveness and implementation of the current legislature in providing ample protection for human health and the environment are key concerns. This review explored a comprehensive summary of pesticides regarding their updated impacts on human health and advanced safety concerns with legislation. Implementing regulations, proper training, and education can help mitigate the negative impacts of pesticide use and promote safer and more sustainable agricultural practices.

The abundance of accessible educational data, supported by the technology-enhanced learning platforms, provides opportunities to mine learning behavior of students, addressing their issues, optimizing the educational environment, and enabling data-driven decision making. Virtual learning environments complement the learning analytics paradigm by effectively providing datasets for analysing and reporting the learning process of students and its reflection and contribution in their respective performances. This study deploys a deep artificial neural network on a set of unique handcrafted features, extracted from the virtual learning environments clickstream data, to predict at-risk students providing measures for early intervention of such cases. The results show the proposed model to achieve a classification accuracy of 84%–93%. We show that a deep artificial neural network outperforms the baseline logistic regression and support vector machine models. While logistic regression achieves an accuracy of 79.82%–85.60%, the support vector machine achieves 79.95%–89.14%. Aligned with the existing studies - our findings demonstrate the inclusion of legacy data and assessment-related data to impact the model significantly. Students interested in accessing the content of the previous lectures are observed to demonstrate better performance. The study intends to assist institutes in formulating a necessary framework for pedagogical support, facilitating higher education decision-making process towards sustainable education.

The outbreak of the COVID-19 pandemic was partially due to the challenge of identifying asymptomatic and presymptomatic carriers of the virus, and thus highlights a strong motivation for diagnostics with high sensitivity that can be rapidly deployed. On the other hand, several concerning SARS-CoV-2 variants, including Omicron, are required to be identified as soon as the samples are identified as 'positive'. Unfortunately, a traditional PCR test does not allow their specific identification. Herein, for the first time, we have developed MOPCS (Methodologies of Photonic CRISPR Sensing), which combines an optical sensing technology-surface plasmon resonance (SPR) with the 'gene scissors' clustered regularly interspaced short palindromic repeat (CRISPR) technique to achieve both high sensitivity and specificity when it comes to measurement of viral variants. MOPCS is a low-cost, CRISPR/Cas12a-system-empowered SPR gene-detecting platform that can analyze viral RNA, without the need for amplification, within 38 min from sample input to results output, and achieve a limit of detection of 15 fM. MOPCS achieves a highly sensitive analysis of SARS-CoV-2, and mutations appear in variants B.1.617.2 (Delta), B.1.1.529 (Omicron) and BA.1 (a subtype of Omicron). This platform was also used to analyze some recently collected patient samples from a local outbreak in China, identified by the Centers for Disease Control and Prevention. This innovative CRISPR-empowered SPR platform will further contribute to the fast, sensitive and accurate detection of target nucleic acid sequences with single-base mutations.

This study was conducted on 3212 Saudi families to investigate the prevalence of consanguineous marriages. The families were interviewed and the information on the relationship between the husband and wife was obtained. The overall rate of consanguinity shows that 57.7% of the families screened were consanguineous. The most frequent were first cousin marriages (28.4%) followed by distant relative marriages (15.2%) and second cousin marriages (14.6%). The families were grouped according to the province of their origin and the consanguinity rates were calculated accordingly. There were slight differences in the consanguinity rates in the five provinces, which ranged from 52.1% to 67.7%. In each province first cousin marriages were the most frequently encountered pattern, ranging from 17.9% to 40.9%. The inbreeding coefficient (F) was calculated for each province and ranged from 0.020 to 0.030. Within each province, there were several significant differences among the populations in the different areas. The highest rate of consanguinity was 80.6% in Samtah and the lowest rate was around 34% in Abha in the South Western province. These results place Saudi Arabia among the countries of the world with a high rate of consanguinity. The possible consequences of increased consanguinity are presented and discussed.

E-learning has become progressively more vital for academia and corporate training and has potentially become one of the most significant developments and applications in Information Technologies (ITs). This study used a quantitative approach seeking a causative explanation of the decision behavior of individuals toward the acceptance and assimilation of e-learning in academic settings. A survey of 286 participants (students) was conducted to collect the research data. Our study framework was based on the third version of the Technology Acceptance Model (i.e., TAM3) and the data were analyzed using structural equation modeling in order to determine the factors that influence the learners’ intention to use e-learning. Results show the predicting (promoting/inhibiting) factors of e-learning technology acceptance, while also examining some related post-implementation interventions expected to contribute to the acceptance and assimilation of e-learning systems. Our results also indicate that TAM3 holds well in the Arabian culture and also outline valuable outcomes such as: managerial interventions and controls for better organizational e-learning management that can lead to greater acceptance and effective utilization. Hopefully, this study provides a roadmap to more understanding of the success factors and post-implementation interventions contributing to the acceptance and assimilation of e-learning systems in developing countries.

Proton exchange membrane water electrolyzer (PEMWE) represents a promising technology for the sustainable production of hydrogen, which is capable of efficiently coupling to intermittent electricity from renewable energy sources (e.g., solar and wind). The technology with compact stack structure has many notable advantages, including large current density, high hydrogen purity, and great conversion efficiency. However, the use of expensive electrocatalysts and construction materials leads to high hydrogen production costs and limited application. In this review, recent advances made in key materials of PEMWE are summarized. First, we present a brief overview about the basic principles, thermodynamics, and reaction kinetics of PEMWE. We then describe the cell components of PEMWE and their respective functions, as well as discuss the research status of key materials such as membrane, electrocatalysts, membrane electrode assemblies, gas diffusion layer, and bipolar plate. We also attempt to clarify the degradation mechanisms of PEMWE under a real operating environment, including catalyst degradation, membrane degradation, bipolar plate degradation, and gas diffusion layer degradation. We finally propose several future directions for developing PEMWE through devoting more efforts to the key materials.

IMPORTANCE: Endovascular intervention for acute ischemic stroke improves revascularization. But trials examining endovascular therapy yielded variable functional outcomes, and the effect of endovascular intervention among subgroups needs better definition. OBJECTIVE: To examine the association between endovascular mechanical thrombectomy and clinical outcomes among patients with acute ischemic stroke. DATA SOURCES: We systematically searched MEDLINE, EMBASE, CINAHL, Google Scholar, and the Cochrane Library without language restriction through August 2015. STUDY SELECTION: Eligible studies were randomized clinical trials of endovascular therapy with mechanical thrombectomy vs standard medical care, which includes the use of intravenous tissue plasminogen activator (tPA). DATA EXTRACTION AND SYNTHESIS: Independent reviewers evaluated the quality of studies and abstracted the data. We calculated odds ratios (ORs) and 95% CIs for all outcomes using random-effects meta-analyses and performed subgroup and sensitivity analyses to examine whether certain imaging, patient, treatment, or study characteristics were associated with improved functional outcome. The strength of the evidence was examined for all outcomes using the GRADE method. MAIN OUTCOMES AND MEASURES: Ordinal improvement across modified Rankin scale (mRS) scores at 90 days, functional independence (mRS score, 0-2), angiographic revascularization at 24 hours, symptomatic intracranial hemorrhage within 90 days, and all-cause mortality at 90 days. RESULTS: Data were included from 8 trials involving 2423 patients (mean [SD] age, 67.4 [14.4] years; 1131 [46.7%] women), including 1313 who underwent endovascular thrombectomy and 1110 who received standard medical care with tPA. In a meta-analysis of these trials, endovascular therapy was associated with a significant proportional treatment benefit across mRS scores (OR, 1.56; 95% CI, 1.14-2.13; P = .005). Functional independence at 90 days (mRS score, 0-2) occurred among 557 of 1293 patients (44.6%; 95% CI, 36.6%-52.8%) in the endovascular therapy group vs 351 of 1094 patients (31.8%; 95% CI, 24.6%-40.0%) in the standard medical care group (risk difference, 12%; 95% CI, 3.8%-20.3%; OR, 1.71; 95% CI, 1.18-2.49; P = .005). Compared with standard medical care, endovascular thrombectomy was associated with significantly higher rates of angiographic revascularization at 24 hours (75.8% vs 34.1%; OR, 6.49; 95% CI, 4.79-8.79; P < .001) but no significant difference in rates of symptomatic intracranial hemorrhage within 90 days (70 events [5.7%] vs 53 events [5.1%]; OR, 1.12; 95% CI, 0.77-1.63; P = .56) or all-cause mortality at 90 days (218 deaths [15.8%] vs 201 deaths [17.8%]; OR, 0.87; 95% CI, 0.68-1.12; P = .27). CONCLUSIONS AND RELEVANCE: Among patients with acute ischemic stroke, endovascular therapy with mechanical thrombectomy vs standard medical care with tPA was associated with improved functional outcomes and higher rates of angiographic revascularization, but no significant difference in symptomatic intracranial hemorrhage or all-cause mortality at 90 days.