Search Results (212)

The peach fruit fly, Bactrocera zonata (Saunders) (Diptera: Tephritidae), is a climate-sensitive agricultural invader that threatens fruit production across the Arabian Peninsula, yet its realized climatic niche and future exposure under warming remain insufficiently resolved. We used Maximum Entropy (MaxEnt) modeling to quantify current and projected habitat suitability across the region (~3.2 million km²) under two Shared Socioeconomic Pathway scenarios (SSP1-2.6 and SSP5-8.5) for the 2050s and 2070s, based on 55 spatially filtered occurrence records and seven non-collinear environmental predictors, with sampling bias controlled using a Gaussian kernel density bias file. Model performance was robust, with mean training AUC of 0.922 ± 0.011 (SD) and mean TSS of 0.538 ± 0.115 (SD; range: 0.368–0.692), indicating moderate variability across replicates. Suitability was governed primarily by elevation, mean temperature of the driest quarter (Bio 9), mean diurnal temperature range (Bio 2), and precipitation of the coldest quarter (Bio 19), which together contributed over 97% of the model output, indicating strong climatic and topographic control on range persistence. Under present conditions, 790,714 km², or 28.38% of the study area, was suitable, concentrated in the southwestern highlands of Saudi Arabia and Yemen, the Omani mountain ranges, and coastal fringes of the Arabian Gulf and Gulf of Oman. Future projections showed a consistent net contraction of suitable habitat across all scenarios, from 7.4% under SSP1-2.6 in the 2050s to 28.0% under SSP5-8.5 in the 2070s. In all cases, contraction exceeded expansion, although the eastern Omani highlands remained a potential climatic refugium. These patterns indicate that warming is likely to reorganize rather than uniformly expand suitability, providing a spatial basis for climate-informed biosecurity, surveillance, and regional pest management. Full article

Background: Colorectal cancer (CRC) represents a major global health burden, accounting for roughly 10% of all newly diagnosed cancers and cancer-related deaths worldwide. According to the World Health Organization, it is the third most diagnosed malignancy and the second leading cause of cancer mortality. Postoperative complications remain a significant concern after CRC resection, occurring in up to 50% of patients and contributing to increased morbidity, mortality, prolonged hospitalization, and substantial healthcare expenditure. Artificial intelligence (AI) has emerged as a transformative tool in modern healthcare, offering advanced capabilities in predictive analytics, clinical decision support, and personalized perioperative management. Methods: This review systematically evaluates the application of AI, specifically machine learning (ML) and deep learning (DL) algorithms, in the prediction of anastomotic leak (AL) and other major postoperative complications. In this context, AI models are generally used to refine risk stratification and enhance surgical decision-making. Results: A total of 13 studies were included, encompassing 15,105 patients. Across these studies, ML and DL algorithms consistently outperformed conventional statistical models in forecasting postoperative outcomes. Conclussions: Current evidence suggests that AI has substantial potential to improve perioperative risk prediction, support intraoperative decision-making, and personalize postoperative surveillance in patients undergoing CRC surgery. Methodological limitations, including a high risk of bias, limited external validation, heterogeneous outcome definitions, and inconsistent reporting, necessitate more robust, prospective, multicenter research before widespread clinical adoption can be realized. Full article

Infrared small object detection (ISOD) plays a crucial role in military reconnaissance, security surveillance, and remote sensing monitoring, where weak thermal responses and complex backgrounds impose significant challenges. The recent self-supervised vision foundation model DINOv3 has demonstrated remarkable generalization ability across various visual tasks. However, directly transferring it to ISOD still remains challenging due to substantial cross-domain discrepancy between visible and infrared imagery, as well as the limited granularity of foundation features in capturing subtle thermal variations. To address these issues, this study proposes a spatial–frequency inductive bias-guided network (SFI-Net) based on DINOv3 for cross-domain representation learning in infrared small object detection. Instead of conventional domain adaptation strategies, SFI-Net explicitly models infrared-specific inductive biases in both spatial and frequency domains to enhance transferred representations. First, a spatio-frequency hybrid adapter (SFHA) is designed and embedded across multiple layers of the frozen backbone to learn infrared-specific inductive biases within distinct subspaces. Second, a feature compensation strategy with an auxiliary convolutional branch is devised to compensate for the limitation of DINOv3 in capturing multi-scale fine-grained features. Extensive experiments on the IRSTD-1K and NUDT-SIRST datasets demonstrate that the proposed SFI-Net outperforms state-of-the-art methods in both detection accuracy and computational efficiency while exhibiting strong cross-scenario generalization capability. Full article

Background/Objectives: Artificial intelligence (AI) is rapidly transforming healthcare. Its integration into community health nursing—a discipline centered on population-level prevention, health promotion, and primary care in community settings—remains insufficiently explored. This narrative review examines the opportunities, ethical challenges, and future directions for integrating AI into community health nursing education and practice. Methods: A literature search was conducted across PubMed, CINAHL, Scopus, Web of Science, and IEEE Xplore for publications between January 2017 and March 2026. The initial search yielded 612 records; after the removal of duplicates and screening of titles, abstracts, and full texts against predefined criteria, 58 sources were retained for thematic synthesis, comprising empirical studies, systematic and umbrella reviews, scoping reviews, meta-analyses, and authoritative policy documents. Screening and data extraction were performed by two reviewers, with disagreements resolved by discussion. Results: AI offers opportunities for community health nursing across four interconnected domains: clinical decision support for community-based assessments, predictive analytics for population health management, enhanced disease surveillance and outbreak detection, and personalized health education delivery. Significant challenges persist, including algorithmic bias, data privacy concerns, threats to the therapeutic nurse–client relationship, inadequate AI literacy among nursing faculty, and regulatory gaps. Most empirical evidence originates from hospital or general nursing settings; transferability to community contexts is therefore inferred rather than directly demonstrated. Conclusions: Responsible integration of AI into community health nursing requires curriculum reform, ethical governance frameworks, faculty development, equitable access, and interdisciplinary collaboration. AI should augment, not replace, the relational and culturally sensitive care that defines this discipline. Given the narrative nature of the review and the limited community-specific evidence, conclusions are framed as a vision of the AI–community health nursing interface rather than a definitive synthesis. Full article

Upper tract urothelial carcinoma (UTUC) presents distinct diagnostic and therapeutic challenges because of its rarity, anatomic constraints, frequent understaging at biopsy, and risk of systemic recurrence after radical nephroureterectomy. Current perioperative management is driven primarily by clinicopathologic risk factors, which may be insufficient to identify occult molecular residual disease (MRD) or to determine which patients are most likely to benefit from systemic therapy. This narrative review summarizes available evidence on circulating tumor DNA (ctDNA) in UTUC and related urothelial carcinoma settings, classifies the level of evidence supporting each application, and proposes a research framework for prospective evaluation. The strongest UTUC-specific evidence supports ctDNA as a prognostic biomarker associated with recurrence risk, whereas predictive validity for selecting chemotherapy, immune checkpoint inhibitors, antibody-drug conjugates, targeted therapy, or surveillance intensity remains unproven. Evidence from muscle-invasive bladder cancer, including ctDNA-correlative and ctDNA-guided perioperative trials, provides biologic rationale but should not be directly translated into routine UTUC care without disease-specific validation. We outline key implementation questions, including target population, assay selection, timing, false-positive and false-negative results, lead-time bias, and integration of plasma ctDNA with utDNA. Prospective UTUC-specific trials are needed to determine whether ctDNA-guided perioperative strategies improve survival, reduce unnecessary toxicity, and are cost-effective. Full article

Background: Streptococcus pneumoniae may asymptomatically colonize the human nasopharynx and remains a leading cause of invasive and noninvasive disease in children, accounting for an estimated 294,000 global deaths in those aged under five years. Nationally representative serotype data from Indonesia remain limited despite national PCV13 rollout in 2022. This study aims to evaluate the distribution of Streptococcus pneumoniae serotypes and estimate the coverage of pneumococcal conjugate vaccines (PCVs) among children aged 0–18 years in Indonesia. Methods: Systematic search of PubMed, Scopus, ScienceDirect, Google Scholar, and Paediatrica Indonesiana (to December 2025) for observational studies (PROSPERO CRD420251239935). The extracted data included the study period, setting, population, specimen type, serotypes, sample size, and nasopharyngeal carriage. Pooled serotype prevalence is calculated; vaccine coverage estimated for pneumococcal conjugate vaccines containing 10 (PCV10), 13 (PCV13), 15 (PCV15), and 20 (PCV20) serotypes assuming vaccine-type priority in multicolonization. Risk of bias assessed using the Joanna Briggs Institute’s checklist for prevalence studies. Results: Nineteen studies across 13 regions of Indonesia involving children aged 0–18 years included. Nasopharyngeal carriage ranged from 21.0% to 87.6% in healthy children and 9.2% to 73% in children with illnesses. The most common serotypes were 19F, 23F, 6B, 14, 19A, and 34. Non-typeable isolates accounted for more than 20% of all isolates in several studies. The pooled coverage for PCV10, PCV13, PCV15, and PCV20 was 40.3%, 50.2%, 50.8%, and 57.0% respectively. Low-moderate RoB (63% low). Conclusions: The dominant serotypes largely included in PCV13. Active surveillance is required to monitor serotype shifts and ensure the long-term effectiveness of the national vaccination program. Full article

Ventilator-associated pneumonia (VAP) remains a major healthcare-associated infection in intensive care units (ICUs) and is associated with prolonged hospitalization, increased antimicrobial use, and high mortality. In Saudi Arabia, evidence on VAP epidemiology, microbiology, and outcomes is fragmented across settings. This study aimed to systematically review and synthesise the available evidence on VAP in Saudi Arabian ICUs. This study followed PRISMA guidelines and was prospectively registered in PROSPERO (CRD420261332740). A systematic search of PubMed, MEDLINE, Embase, and Web of Science was conducted for studies published between 2015 and 2025. Studies from Saudi Arabia reporting VAP incidence in ICUs were included. A random-effects model was used to pool incidence per 1000 ventilator-days. Risk of bias was assessed using Joanna Briggs Institute tools. Analyses were performed using R. Seven studies involving a total of 15,844 patients, representing multicentre studies, national surveillance data, and single-centre cohorts across diverse ICU settings. The pooled incidence of VAP was 8.50 episodes per 1000 ventilator-days (95% CI: 3.23–13.78), with substantial heterogeneity (I² = 97%). Subgroup analysis showed higher incidence during baseline phases (12.46 per 1000 ventilator-days) compared with intervention phases (8.06 per 1000 ventilator-days), while surveillance estimates were lower. Gram-negative pathogens predominated, particularly Acinetobacter baumannii, often exhibiting multidrug resistance. VAP was associated with prolonged ICU stay, delayed extubation, and high mortality. Implementation of infection prevention bundles was associated with reductions in VAP incidence. Ventilator-associated pneumonia remains a significant burden in Saudi Arabian ICUs, characterised by substantial variability in incidence and a predominance of multidrug-resistant pathogens. Strengthening infection prevention measures, enhancing antimicrobial stewardship, and improving national surveillance systems are essential to reduce VAP burden and improve patient outcomes. Full article

Background/Objectives: This study aimed to estimate self-reported prevalence of HIV and gonorrhea among primary healthcare attendees in Riyadh and to identify key demographic, behavioral, and clinical predictors, acknowledging that diagnoses were based on participant self-report rather than laboratory confirmation. Methods: A cross-sectional survey was conducted between March and July 2023 across 48 primary healthcare centers in Riyadh. A total of 14,239 adult participants (aged ≥18 years) completed an electronically administered questionnaire that included self-reported prior diagnoses of HIV and gonorrhea. Multivariable logistic regression models were used to identify independent predictors of self-reported HIV and gonorrhea. Results: The self-reported prevalence of HIV was 2.6% (95% CI: 2.35–2.87%), and gonorrhea was 3.1% (95% CI: 2.83–3.40%). Several factors were independently associated with higher odds of self-reported infection. Younger age (<50 years) increased risk (HIV: AOR = 2.19; gonorrhea: AOR = 1.57), as did female sex (HIV: AOR = 1.67; gonorrhea: AOR = 1.59), higher education (HIV: AOR = 1.29; gonorrhea: AOR = 1.23), married status (HIV: AOR = 1.76; gonorrhea: AOR = 1.49), and insurance coverage (HIV: AOR = 2.01; gonorrhea: AOR = 1.88). Behavioral and clinical factors included smoking (HIV: AOR = 4.79; gonorrhea: AOR = 2.41), hypertension (HIV: AOR = 2.58; gonorrhea: AOR = 1.49), obesity (HIV: AOR = 11.55; gonorrhea: AOR = 9.02), hypercholesterolemia (HIV: AOR = 2.24; gonorrhea: AOR = 2.53), and heart disease (HIV: AOR = 11.31; gonorrhea: AOR = 8.77). The notably high associations for obesity and heart disease should be interpreted with caution, as they may be influenced by detection bias or residual confounding within the healthcare-seeking sample. Conclusions: This study provides key insights into the self-reported burden and predictors of HIV and gonorrhea in Saudi Arabia. While identifying significant demographic and metabolic risk profiles, the high magnitude of certain clinical associations must be interpreted with caution due to potential detection bias and residual confounding. Given the reliance on self-reported data, these findings should be viewed as an epidemiological baseline rather than absolute prevalence. Prioritizing clinical context over statistical values and strengthening integrated, laboratory-based surveillance within primary care will be essential for improving early detection and evidence-based prevention strategies in the region. Full article

Background: Wastewater-based epidemiology (WBE) is a valuable population-level surveillance tool for monitoring SARS-CoV-2 circulation. However, evidence on optimal viral concentration approaches in confined institutional settings such as prisons remains limited. This study aimed to compare the performance of Direct Capture (DC) and Electronegative Membrane Filtration (EMF) for SARS-CoV-2 RNA detection in wastewater from a prison facility in Selangor, Malaysia. Methods: Composite wastewater samples collected over 18 weeks (April–August 2023; n = 50) were analysed by RT-dPCR targeting the N1 and N2 gene regions, with concentrations normalized to pepper mild mottle virus (PMMoV). DC consistently outperformed EMF across both gene targets. Median concentrations obtained using DC were 11.09 × 10³ copies L⁻¹ (N1) and 3.43 × 10³ copies L⁻¹ (N2), compared with 0.70 × 10³ copies L⁻¹ (N1) and 0.48 × 10³ copies L⁻¹ (N2) using EMF. Detection frequencies were higher with DC (N1: 94%, N2: 84%) than with EMF (N1: 88%, N2: 76%). Paired statistical analysis confirmed significant differences between methods (N1: p = 2.3 × 10⁻⁷; N2: p = 9.4 × 10⁻⁵), and Bland–Altman analysis demonstrated systematic underestimation by EMF (mean bias −1.15 log₁₀ for N1; −0.87 log₁₀ for N2), indicating that the methods are not analytically interchangeable. Conclusions: Normalization reduced absolute SARS-CoV-2 RNA concentrations while preserving temporal trends, supporting its use to improve comparability across sampling periods. Overall, these findings demonstrate that DC combined with N1 detection provides a more sensitive and reliable approach for SARS-CoV-2 WBE in confined settings, underscoring the importance of methodological optimization to strengthen early-warning capacity in high-risk environments. Full article

Monitoring both captive animals and wild populations is necessary to ensure adequate animal welfare and wildlife conservation. Existing monitoring tools, e.g., camera traps, enable surveillance, yet analysis can prove time-consuming and labor-intensive if handled manually. The automated nature of machine learning (ML) reduces observer bias and manual workload and improves assessment capacity of behavioral monitoring tools that are often used by staff at zoological institutions. This study investigated the activity and space use of three captive jaguars (Panthera onca) through automated individual recognition, activity tracking, and heatmap visualization using an ML model trained on video footage. In total, 123.8 h of video footage was recorded of the jaguar enclosure in Randers Regnskov, Tropical Zoo. The ML model analyzed all videos containing jaguars from one day. The model achieved satisfactory performance based on its evaluation metrics (mean average precision, recall, precision, and F1-score). The ML model showed repeated movement tracks within specific enclosure areas. The jaguars exhibited significantly more inactive than active behavior and did not seem to exhibit natural bimodal nocturnal or crepuscular hunter activity patterns. It should be stated that, due to the small sample size of only three jaguars and 24 analyzed hours, this study is a proof-of-concept to demonstrate the potential of ML methods as valuable tools for individual recognition, activity tracking, and monitoring of space use to aid in future animal welfare monitoring. Full article

Background/Objectives: Active surveillance (AS) is recommended for men with low-risk prostate cancer to minimize overtreatment while monitoring for disease progression. However, current surveillance strategies rely heavily on repeat biopsies, which are invasive and associated with morbidity. MyProstateScore 2.0—Active Surveillance (MPS2-AS) is a urine-based biomarker test developed to predict progression to Grade Group ≥ 2 (GG ≥ 2) and Grade Group ≥ 3 (GG ≥ 3) prostate cancers in men on AS. The objective of this study was to analytically validate the reproducibility and robustness of MPS2-AS analyte detection and risk score calculation across key laboratory variables. Methods: Analytical precision was evaluated using pooled urine specimens processed using the MPS2-AS laboratory workflow. Eight pooled urine samples were tested in a within-laboratory design across five days, with two runs per day, and two replicates per run. Additional reproducibility studies assessed variability across three QuantStudio™ 12K Flex Real-Time PCR Systems and three OpenArray™ chip lots. Ten RNA biomarkers were quantified by RT-PCR and used to calculate the MPS2-AS GG1-2 and GG1-3 risk scores. Variance components were estimated using hierarchical ANOVA. Results: The MPS2-AS analyte measurements demonstrated high precision across within-laboratory testing, with standard deviations ranging from 0.00 to 0.60 and coefficients of variation (%CV) from 0.00 to 4.01%. The reproducibility across qPCR instruments and OpenArray chip lots showed similar robustness, with analyte %CVs of ≤4.57% and ≤4.10%, respectively. These stable analyte measurements translated to reproducible model outputs, with %CV ≤ 10.69% for the GG1-2 risk score and ≤7.20% for the GG1-3 risk score across all tested conditions. No systematic bias was observed between runs, days, instruments, or reagent lots. Conclusions: MPS2-AS demonstrates strong analytical precision and reproducibility for quantifying urinary biomarkers and generating GG1-2 and GG1-3 risk scores. These results support the reliability of MPS2-AS for clinical laboratory implementation and its use as a non-invasive tool to inform biopsy decisions in men with Grade Group 1 prostate cancer undergoing active surveillance. Full article

Background/Objectives: Obesity is a chronic, relapsing disease with a widening gap between clinical need and the availability of specialist care. Artificial intelligence (AI) may enable earlier risk detection, more precise phenotyping, and scalable behavioural support across obesity treatment pathways. This narrative review synthesises contemporary AI applications across the obesity care continuum and evaluates their translational readiness. Methods: A targeted search of PubMed/MEDLINE and Google Scholar (January 2024–January 2026) was conducted, complemented by citation chaining. Evidence was synthesised across four domains: (1) risk prediction and screening, (2) environmental and behavioural determinants, (3) multimodal phenotyping and precision stratification, and (4) AI-enabled lifestyle interventions and behavioural coaching (AIBC). Results: Electronic health record (EHR)-based models demonstrate clinically useful discrimination for early risk identification. Multimodal approaches refine stratification beyond body mass index (BMI)-centric classification. AI-enabled behavioural coaching (AIBC) platforms show emerging evidence of clinically meaningful weight loss, including non-inferiority to human coaching; however, long-term effectiveness, generalisability, and equity remain insufficiently established. Conclusions: AI is positioned to become a core enabler of personalised obesity pathways. Safe translation requires external validation, bias auditing, transparent reporting, human oversight, and post-deployment surveillance aligned with clinical guidelines and regulatory expectations. Full article

Video synthetic aperture radar (ViSAR) provides continuous multiframe images while maintaining high resolution and has become an important tool for complex scene surveillance and moving target tracking. ViSAR imaging is susceptible to interframe drift caused by motion errors, which severely degrades video stability. When registering long time series of real airborne video SAR images, conventional image registration based on Normalized Cross-Correlation (NCC) is affected by several factors, including platform residual motion errors, approximations in the imaging geometry, interpolation resampling, and SAR speckle noise. As a result, noticeable interframe jitter persists in the registered sequence, and the stabilization accuracy is insufficient to meet high-precision image stabilization requirements. To address these issues, this paper proposes an image stabilization method for airborne video SAR based on a joint Singer-random walk model. Firstly, with the first frame selected as the reference, subpixel drift measurements in the azimuth and range directions are extracted from continuous frames via NCC-based registration. Subsequently, the true drift is modeled as a two-dimensional Singer process and the systematic bias as a random walk process, yielding a joint state space model that comprises displacement, velocity, acceleration, and bias components. On this basis, a Kalman filter and a Rauch–Tung–Striebel (RTS) fixed-interval smoother are applied to perform temporal filtering and trajectory smoothing on the drift measurements, thereby producing smooth two-dimensional drift estimates that closely approximate the actual drift trajectory. Finally, the smoothed drift trajectory is used to perform frame-by-frame subpixel drift correction on the original image sequence, achieving high-precision interframe stabilization of the ViSAR imagery. The results of real data processing demonstrate that the proposed method can effectively improve the consistency and scene stability of ViSAR multi-frame imaging. Full article

Background: Percutaneous liver biopsy is a cornerstone in the diagnostic and therapeutic management of pediatric liver diseases. However, data on the optimal needle gauge for coaxial techniques in children remain scarce. Smaller-gauge needles may theoretically enhance safety but could potentially compromise diagnostic yield. Objectives: The primary objective of this study was to evaluate and compare the safety and diagnostic clinical adequacy of ultrasound-guided percutaneous liver biopsies performed with semi-automated 20G versus 18G coaxial needles in pediatric patients. Patients and Methods: This retrospective cohort study included consecutive patients aged ≤19 years who underwent percutaneous non-targeted liver biopsies at a tertiary medical center between 2006 and 2012. Patient demographics, biopsy technique parameters (including needle gauge, number of cores, and tract embolization), and procedure-related complications were analyzed. Procedural success was defined by diagnostic and clinical adequacy, requiring a definitive pathology report and the presence of ≥7 portal tracts (the widely accepted threshold for a reliable histologic diagnosis). Complications were classified according to the Society of Interventional Radiology guidelines. Results: A total of 320 biopsies were performed in 260 patients (44.6% female; mean age 7.4 ± 6.0 years). Common indications included post-liver transplantation surveillance (28.4%) and unexplained liver enzyme elevation (22.5%). Biopsies were performed using 18G (n = 148; 46.3%) or 20G (n = 172; 53.7%) coaxial needles. Diagnostic and clinical adequacy was achieved in 100% of the procedures, with biopsy results directly influencing clinical management in 39.7% of cases. The overall complication rate was 5.3% (3.4% minor, 1.9% major), with no procedure-related mortality. While raw complication rates were numerically higher in the 20G group (likely to reflect an operator-driven selection bias for younger or higher-risk patients), the differences between the 18G and 20G needles were not statistically significant. Notably, the use of the 20G needle was associated with a significantly reduced clinical need for post-biopsy tract embolization. Conclusions: Our findings demonstrate no statistically significant differences in complication rates or diagnostic clinical adequacy between 18G and 20G coaxial needles for pediatric percutaneous liver biopsies. When selected based on appropriate clinical judgment, the 20G needle provides a high diagnostic yield and serves as an effective option, particularly for reducing the need for tract embolization. However, both 18G and 20G needles represent acceptable clinical options within the pediatric interventional armamentarium. Ultimately, the choice of needle gauge should be meticulously tailored to individual patient characteristics, bleeding risk profiles, and specific clinical indications, rather than uniformly recommending a smaller gauge across all pediatric age groups. Full article

Background: High infectious disease burden and uncontrolled antibiotic usage across human, animal, and environmental contaminants make antimicrobial resistance (AMR) a growing public health problem in Africa. Mobile genetic elements (MGEs) such plasmids, transposons, integrons, conjugative elements, and phages help spread AMR via horizontal gene transfer (HGT) across human, animal, food, and environmental sources. Despite growing evidence for antibiotic resistance genes (ARGs), Africa lacks a one-health-focused synthesis of mobile genetic element-mediated AMR. Objective: This systematic review and meta-analysis aimed to consolidate information on MGEs and ARGs in AMR dissemination throughout Africa’s one health interface. Methods: The literature was searched using PubMed, Scopus, and ScienceDirect. Observational. molecular epidemiology, whole genome sequencing (WGS), and metagenomic investigations of MGE-associated AMR in Africa were eligible. The study selection, data extraction, and quality assessment were performed by two independent reviewer and quality was graded using ROBVIS 2 utilizing Rayyan software. Narrative synthesis, random-effect meta-analysis, subgroup analysis, and meta-regression were utilized. Results: A total of 109 studies were included, with 91 studies contributing to the meta-analysis. MGEs reported were plasmids (71.7%) and integrons (54.8%). ARGs carried by MGEs were blaCTMX-M-15 (78.6%), Sul2 (69.6%), blaTEM (59.1%), and tetA (49.9%). Horizontal gene transfer was seen in 259 instances; however, transmission was unclear. In 442 observations, transmission pathways across human, animal, and environmental interfaces showed AMR prevalence of 75.1% in human, 98.0% in human–animal, and 61.3% in one health interface. Whole-genome sequencing was the most frequently used method for detecting MGEsThe pooled pathogen and AMR prevalence rates were 73.3% (95% CI: 60.5–83.7%) and 94% (95% CI: 85–98%), with significant heterogeneity (I² = 97.8% and 97.4%, respectively). The prevalence of Escherichia coli was 93% and Salmonella enterica 85% in subgroup analysis. Fluoroquinolones, aminoglycosides, and beta-lactams were prevalent in humans (89.7%) and human–animal interactions (98.0%) according to AMR Class. Conclusions: Horizontal gene transfer has propagated MGE-mediated antimicrobial resistance across human, animal, and environmental interfaces in Africa. To combat AMR in Africa, coordinated, genomics-informed One Health surveillance and antibiotic stewardship are needed. Due to variability and publication bias, these data should be considered cautiously. Pooled data may only show descriptive patterns, and not necessarily precise continent-wide prevalence estimates. Full article