Conventional femtosecond coherent laser spectroscopy predominantly focuses on the temporal phase coherence through time- or frequency-resolved methods. In this Letter, we suggest an alternative experimental framework based on spatial phase coherence. The intrinsic spectral dispersion of wave vectors in femtosecond pulses and sample dimensions exceeding the laser wavelength creates a compelling basis to establish spatial phase coherence as a novel approach to femtosecond laser spectroscopy. Using rotational Raman coherence in air as a case study, we analyze the transverse spatial distribution of the third-order signal generated by the rotational wave packet. Our findings reveal apparent temporal shifts and distortions in time-resolved signals that arise in conventional measurements lacking sensitivity to spatial phase coherence. Moreover, we demonstrate that spatial phase coherence can serve as a useful tool for thermometric applications, showcasing its sensitivity to temperature variations. These discoveries open new avenues in femtosecond laser spectroscopy, including an alternative single-shot detection scheme, a new form of Raman coherence imaging, and molecular species quantification during overlapping fractional revivals.

We address the security of a network of Connected and Automated Vehicles (CAVs) cooperating to safely navigate through a conflict area (e.g., traffic intersections, merging roadways, roundabouts). Previous studies have shown that such a network can be targeted by adversarial attacks causing traffic jams or safety violations resulting in collisions. We focus on attacks targeting the V2X communication network used to share vehicle data and consider uncertainties as well due to noise in sensor measurements and communication channels. To combat these, motivated by recent work on the safe control of CAVs, we propose a trust-aware robust event-triggered decentralized control and coordination framework that can provably guarantee safety. We maintain a trust metric for each vehicle in the network computed based on their behavior and used to balance the tradeoff between conservativeness (when deeming every vehicle as untrustworthy) while guaranteeing safety and performance. It is important to highlight that our framework is invariant to the specific choice of the trust framework. Moreover, we show that our proposed trust framework is immune to false positives. Based on this framework, we propose an attack detection and mitigation scheme which provably guarantees safety against false positive cases which may arise from a poor choice of trust framework. We use extensive simulations in SUMO and CARLA to validate the theoretical guarantees and demonstrate the efficacy of our proposed scheme to detect and mitigate adversarial attacks. The code for the simulated scenarios are available at https://github.com/SabbirAhmad26/Trust_based_CBF .

Light polarization states are the intrinsic properties of light and are widely exploited in fields such as remote sensing, computing, and medical imaging. Conventional photoelectric sensors used for polarization detection are fundamentally constrained by the wavelength-dependent bandgap of their constituent materials, and therefore hinder their ability to meet the practical demands of broadband high-contrast polarization detection. To address this limitation, we develop a time-division photoacoustic sensing-based polarization detection (PSPD) scheme that exploits the polarization-dependent photoacoustic response of an optically anisotropic absorbing transducer at a wide spectral range, enabling broadband polarization detection from the visible to the second near-infrared window (NIR-II) with high polarization contrast. Moreover, leveraging the depth-resolved nature of the photoacoustic effect, we show that the z-axis extension of PSPD can facilitate fast multiplexed polarization readout toward polarization imaging. The proposed PSPD offers a versatile platform for fast polarization measurements, enabling future developments in polarization imaging and advanced optical sensing applications.

The Davos Alzheimer's Collaborative (DAC) Egypt Cohort (DAC-Egypt) is a newly established longitudinal study of cognitive aging in a community-based convenience sample of older Egyptian adults. The cohort's purpose is to characterize trajectories of cognitive decline and dementia risk factors in an understudied population, filling a critical gap in aging research in the Middle East. Participants (n = 1,530) aged 55 and above were recruited via regionally diverse convenience sampling, with detailed baseline data collected on demographics, health status, lifestyle, and cognitive function. Cognitive assessments included both traditional neuropsychological testing and innovative digital tools (digital voice/speech & olfactory-sensory assessments) to enable comprehensive monitoring. Key preliminary findings indicated a high prevalence of chronic diseases and notable socioeconomic disparities in cognitive performance among older Egyptians. Blood samples were collected from 98% of participants, and dried blood spot (DBS) cards were obtained for 88% of participants to facilitate future biomarker and genetic research. This study seeks to enrich the scientific field of dementia and Alzheimer's disease and related disorders (ADRD) for early detection and intervention strategies for cognitive health in aging populations.

Source code plagiarism detection plays an indispensable role in computer education. Over the past few decades, various tools and systems have been developed to solve software plagiarism detection with relatively satisfactory results. However, hardware description language (HDL) plagiarism detection has become a new challenge with the popularity of FPGA-based digital circuit design courses. Constructing a Verilog HDL plagiarism detection approach that supports fast incremental detection on large samples has become a thorny issue. This article proposes a scalable approach to achieve Verilog HDL plagiarism detection that supports incremental detection on large samples. The method employs a dual-layer detection strategy at both the submission-level and the module-level. At the submission-level, it conducts project-wide comparisons, while at the module-level, it calculates a project’s suspicious score through a prefix matching-based risk assessment mechanism. This approach effectively distinguishes common modules from potential plagiarism, significantly reducing the false positive rate. Our method detects more plagiarism pairs with higher accuracy than the widely used Measure of Software Similarity (Moss) system. To further validate the effectiveness of our approach, we conducted a comparative analysis with an ANTLR-based syntax-aware detector. On the dataset of Verilog HDL projects from a computer organization course (Fall 2023 and Fall 2024), our method achieved higher precision and reduced time consumption compared to the ANTLR-based model. Initially, this study leverages filtered token features and the locality-sensitive hashing algorithm to transform Verilog HDL projects into fingerprints. An efficient index is constructed to accelerate both incremental detection and batch detection. Four strategies for filtering N-Gram token sequences are used in this study. The proposed fast incremental Verilog HDL plagiarism detection approach has a logarithmic level of time complexity. Experiments were conducted to find and validate the optimal parameters for the proposed strategies on real-world datasets. The experimental results show that combining our method with Moss increases the convenience and accuracy of Verilog HDL plagiarism detection in real courses.

Orofacial tumours represent a significant health challenge in Ghana impacting functions and quality of life. Despite their importance, there is a dearth in literature on the epidemiological patterns of these neoplasms in Ghana. This study aims to bridge this knowledge gap by investigating their characteristics at a major teaching hospital in Ghana. A retrospective, cross-sectional study was conducted on 204 patients with consecutive sampling. Data were retrieved from the Lightwave Health Information Management System. Ethical approval was obtained from the Institutional Review Board of Komfo Anokye Teaching Hospital. Descriptive statistics were used to summarize the data. Chi-square tests were performed to assess associations between age, gender and tumour type. The study included 204 patients with a mean age of 40.24±22.268 years. There was a slight female predominance (51.0%) with a male-to-female ratio of 1:1.04. Benign tumours (59.8%) were more prevalent than the malignant ones (40.2%). The highest incidence of orofacial tumours was observed in the 5th decade of life (16.7%). The mandible was the commonest site of tumour occurrence (18%), followed by the tongue (12%). A significant association was found between age and tumour type (p<0.001), with benign tumours more common in younger age groups (peak in 31-40 years, 18.9%) and malignant tumours more prevalent in older individuals (peak in 61-70 years, 24.4%). This study provides valuable insights into the epidemiology of orofacial tumours in Ghana, highlighting the importance of age-specific screening and early detection strategies. The findings contribute to the understanding of orofacial tumour patterns in Ghana and underscore the need for further research to elucidate underlying factors contributing to these patterns.

Extracellular vesicles (EVs) are promising biomarkers for liquid biopsy, but their clinical application is limited by intrinsic heterogeneity and the lack of methods capable of resolving functionally distinct EV subpopulations at the single-vesicle level. Conventional bulk analyses obscure rare but clinically relevant EV subsets, while most single-EV approaches focus on physical properties or surface markers, with limited access to intravesicular functional information. Here, we report a fusion-enabled EV detection strategy at the single-particle level for functional profiling of macrophage-derived EVs. Liposomal probes encapsulating L-arginine, NADPH, and a nitric oxide (NO)-responsive fluorescent dye are engineered to fuse with EV membranes, delivering substrates into the vesicle lumen. In macrophage-derived EVs, inducible nitric oxide synthase (iNOS) catalyzes NO production, activating the fluorescent probe and generating a localized signal within individual vesicles. Signal generation is confined to vesicle-restricted reactions, ensuring specificity and minimizing background. The formation of hybrid vesicles further facilitates optical detection using conventional fluorescence microscopy.

Chronic kidney disease (CKD) affects thyroid metabolism, increasing the risk of hypothyroidism and hyperthyroidism. Dialysis further alters hormone levels through impaired synthesis, iodine imbalance, and treatment-related factors. This study aimed to evaluate the prevalence and incidence of hypothyroidism and hyperthyroidism in patients with CKD on dialysis. We conducted a search in the Scopus and PubMed databases up to November 29, 2024. We included studies reporting prevalence or incidence data on hypothyroidism or hyperthyroidism (clinical or subclinical) in CKD patients on dialysis (hemodialysis or peritoneal dialysis). A random-effects model meta-analysis of proportions was used to calculate pooled prevalence estimates. A total of 39 studies were included, with sample sizes ranging from 40 to 8840 participants. The prevalence of hypothyroidism was 15.9% (95% CI: 13.0 to 19.0; I²: 96.2%), with 5.6% for clinical hypothyroidism and 11.2% for subclinical hypothyroidism (notably higher in peritoneal dialysis). The prevalence of hyperthyroidism was 5.1% (95% CI: 2.7 to 8.2; I²: 93.8%), with 0.9% for clinical hyperthyroidism and 3.3% for subclinical hyperthyroidism. Meta-regression analyses indicated that a longer mean duration on dialysis was associated with a lower prevalence of hypothyroidism (p = 0.008). Only one study reported the incidence of hypothyroidism (10.9%) and hyperthyroidism (4.9%), thus evidence on incidence remains scarce. The prevalence of hypothyroidism and hyperthyroidism in patients with CKD on dialysis is high, although with high heterogeneity among studies. Evidence on incidence remains limited and should be interpreted as exploratory. These findings support increased clinical vigilance and consideration of early detection strategies rather than definitive screening policies.

Nothofagus obliqua forests in south-central Chile are increasingly threatened by outbreaks of a native leaf-miner complex, dominated by the moth Heterobathmia pseuderiocrania. Despite the high ecological and economic value of these forests, landscape-scale monitoring of forest–insect interactions remains limited, particularly regarding the attribution of phenological anomalies to biotic disturbances. This study aimed to detect and quantify the late-2022 outbreak and evaluate its effects on Land Surface Phenology (LSP), addressing signal attribution challenges associated with remote-sensing-based monitoring of insect defoliation. Using MODIS Enhanced Vegetation Index (EVI) time series (2003–2024), Seasonal-Trend decomposition (STL) was applied to isolate long-term trend anomalies. An EVI condition index was developed to compare 2022–2023 observations against a historical baseline, and synchrony between vegetation condition loss and larval developmental phases was assessed. Additionally, Highest Density Regions (HDR) were used to quantify the statistical probability of spectral anomalies. Results revealed a sharp decline in EVI trend during late 2022, reaching the lowest recorded value in the 20-year time series. Phenological decoupling began in November, coinciding with larval development and peak defoliation, with impacts extending across two growing seasons. Ecosystem condition declined to a minimum of 42%, falling with the 4% historical probability region. Notably, exceptional pre-outbreak vigor (160% condition) preceded the disturbance. By integrating spectral anomaly detection with insect life-cycle dynamics, this multi-layered approach strengthens biotic disturbance attribution and provides a scalable framework for remote forest health monitoring. The findings also address key knowledge gaps in Southern Hemisphere Forest entomology and improve early detection strategies for native insect outbreaks.

The realization of compact on-chip devices with spectroscopic functionality is one central goal in modern photonics. Here, we present a fiber-interfaced on-chip light cage that combines seamless single-mode fiber integration with extended light-gas interaction lengths, enabling precise quantitative gas spectroscopy. By employing two detection schemes, this work establishes the light cage as a platform for laser absorption spectroscopy, realized through 3D nanoprinting of an anti-resonant hollow-core waveguide with side access. Optical characterization and benchmarking confirm reliable gas detection at ppm-level detection sensitivity in a miniaturized format. The platform offers broad potential for applications in environmental monitoring, medical diagnostics, and quantum technologies, and provides a versatile foundation for the development of next-generation hollow-core architectures.

OBJECTIVE: The study aimed to investigate the available clinical data regarding the most common early manifestations of urticaria and angioedema in conditions such as Sjögren syndrome, dermatomyositis, systemic scleroderma, and systemic lupus erythematosus (SLE). DATA SOURCES: A literature search was conducted of articles published between 2020 and 2024 using databases such as Scopus, Google Scholar, PubMed, and ScienceDirect, along with relevant sources published before 2020. STUDY SELECTION: One hundred two scientific papers were initially considered, with 45 remaining after full evaluation based on inclusion criteria such as publication date, country, journal, and availability. DATA EXTRACTION: Data were extracted by analyzing publication titles, abstracts, headings, introductions, main content, conclusions, and reference lists. DATA SYNTHESIS: The primary symptoms of urticaria and angioedema in connective tissue diseases were synthesized from evidence-based sources, emphasizing clinical cases across systemic lupus erythematosus, dermatomyositis, Sjögren syndrome, and systemic scleroderma. CONCLUSIONS: The prevalence of autoimmune angioedema in SLE ranged from 15% to 30%, with common areas affected being the face, lips, and periorbital region. In Sjögren syndrome, primary urticaria was observed in 26.2% of cases. The prevalence of edema and urticaria as primary symptoms in dermatomyositis remained uncertain, but localized edema of the face and limbs, as well as urticaria in areas like the neck and elbow creases, were noted. Systemic scleroderma often began with Raynaud phenomenon, accompanied by edema of the hands and fingers. Finally, expanding diagnostic criteria to include these symptoms as potential early indicators of autoimmune connective tissue diseases may help improve early detection and treatment strategies for these conditions.

Central Obesity, Characterized By The Accumulation Of Fat Around The Abdomen, Has Been Increasingly Recognized As A Significant Risk Factor For Type 2 Diabetes Mellitus (T2DM). This Systematic Review Aims To Evaluate Between Central Obesity And The Incidence Of T2DM. A Literature Search Was Conducted In July 2025 Using Databases Such As Pubmed, Google Scholar, And Ebsco. The Inclusion Criterias Are: Studies Published In The Last 10 Years, Available In Full Text, And Accessible For Free. Five Relevant Studies Were Reviewed And Synthesized. The Results Consistently Show That Indicators Of Central Obesity, Including Waist Circumference, Waist-To-Hip Ratio, Weight-Adjusted Waist Index (WWI), And Triglyceridemic Waist Phenotype, Are Strongly Associated With The Risk Of T2DM, Outperforming General Obesity Metrics Like BMI. The Visceral Fat Accumulation Plays A Critical Role In Insulin Resistance And Metabolic Dysfunction. These Results Draw Attention To The Value Of Central Obesity Measurements As Essential Components Of Early Detection And Prevention Strategies Aimed At Combating The Global Diabetes Burden.

ABSTRACT Rift Valley fever virus (RVFV), a WHO Blueprint Priority Pathogen, continues to cause major health and economic losses in endemic regions, yet current molecular and serological diagnostics remain inaccessible or insufficiently sensitive in many settings. Surface‐enhanced Raman spectroscopy (SERS) offers high sensitivity and field‐adaptable potential. This study aimed to develop a SERS‐based RVFV detection assay by combining theoretical modelling with experimental validation. A hybrid approach integrating Schrödinger‐bioluminate and density functional theory (DFT) simulations was used to model molecular interactions within the SERS immunoprobe. Experimentally, a sandwich‐type SERS immunoassay was constructed by immobilising recombinant RVFV nucleocapsid protein (rnp) on a SERS‐active substrate to capture RVFV antibodies. A complementary AuNP/spa/4‐MBA immunoprobe was synthesised to bind captured antibodies and generate a measurable Raman signal. The immunoprobe exhibited a detection limit of 1 μg/mL for RVFV antibodies, demonstrating higher sensitivity than previously developed half‐strip lateral flow assay. Theoretical predictions of key 4‐MBA Raman vibrational modes showed good consistency with experimental spectra, supporting the validity of the simulation‐guided design. The assay successfully established the conceptual feasibility of an RVFV‐specific SERS diagnostic platform. This work presents the first SERS‐based detection strategy tailored for RVFV and the first to integrate computational modelling with assay development for this pathogen. Although preliminary, the hybrid theoretical–experimental approach provides a foundation for further optimisation, incorporation of AI‐based spectral analysis and future deployment using miniaturised field‐portable Raman devices.

The increasing issue of infections caused by methicillin-resistant Staphylococcus aureus (MRSA) necessitates rapid and reliable diagnostic methods. While existing RPA-CRISPR/Cas12a platforms have demonstrated potential for MRSA detection, most rely on single-gene targets or require multiple Cas enzymes. Here, we have developed a novel dual gene detection strategy that simultaneously detects the S. aureus specific femA gene and the methicillin-resistant mecA gene in a single RPA-CRISPR/Cas12a reaction. This integrated approach enables clear discrimination between MRSA and methicillin-sensitive Staphylococcus aureus (MSSA) in just 30 min, with results visualized via both fluorescence and lateral flow strips. The assay exhibited high specificity (no cross-reactivity with common pathogens) and a sensitivity of 10 copies/μL, comparable to qPCR. Validation with 39 clinical samples showed 100% concordance with antimicrobial susceptibility testing. Our dual-gene RPA-CRISPR/Cas12a platform represents a significant advancement in point-of-care MRSA diagnostics, offering enhanced accuracy and operational simplicity.

Advances in targeted therapies, immunotherapy, and early detection have revolutionized lung cancer treatment and extended survival. Nonetheless, lung cancer remains highly fatal. Here, we identify knowledge gaps and propose critical areas of future research, aligning with the mission of the AACR Lung Cancer Task Force. We delineate research priorities, including advancing prevention initiatives, enhancing early detection strategies, developing novel treatments, and refining patient stratification. Addressing disparities and increasing efforts on relatively neglected lung cancer subtypes are also essential. Finally, international collaboration, centralized clinical trial databases, novel clinical trial designs, and artificial intelligence-driven analytics should accelerate precision medicine and aid in elucidating drug resistance mechanisms. Together, these efforts promise to improve patient outcomes.

With the deployment and application of the Fifth-Generation (5G) mobile communication technologies and the ongoing research and development of the Sixth-Generation (6G) mobile communication technologies, the space–air–ground–sea integrated network has become the core development vision for future communications. As aerial nodes, Unmanned Aerial Vehicles (UAVs) can be applied in a wide range of scenarios, including emergency rescue, surveying and mapping, environmental monitoring, and communication coverage enhancement. In terms of communication coverage enhancement, the space–air–ground integrated network, with UAVs as a key component, can provide seamless communication coverage for the full-domain three-dimensional space such as remote areas, deserts, and oceans. Benefiting from advantages such as low cost and high flexibility, UAVs have become a critical research focus, and the one-hop Base Station (BS)–relay UAV–slave UAV architecture for communication coverage enhancement has emerged as an important development direction. However, the high mobility and wide coverage characteristics of UAVs also pose significant synchronization challenges. Aiming at the uplink synchronization problem on the sidelink between slave UAVs and the relay UAV, a two-step random-access scheme based on Asynchronous Non-Orthogonal Multiple Access (A-NOMA) is designed to mitigate the Doppler Frequency Offset (DFO), improve access efficiency, reduce resource consumption, and accommodate the asynchrony among different users. This scheme leverages the existing preamble sequences of the Physical Random Access Channel (PRACH) and realizes DFO estimation in combination with the pairing index. On this basis, a Successive Interference Cancellation (SIC) algorithm based on DFO and phase compensation is designed to complete the demodulation of user data. For the downlink synchronization problem on the sidelink between slave UAVs and the relay UAV, the frequency offset estimation performance is improved by redesigning the resource allocation scheme of the Sidelink Synchronization Signal Block (S-SSB). Meanwhile, considering the energy constraint of UAVs, a downsampling-based detection scheme is designed to reduce UAV power consumption, and a full-link algorithm is developed to support the practical implementation of the proposed scheme.

Current diagnostic approaches for Alzheimer's disease (AD), including neuroimaging and cerebrospinal fluid (CSF) analysis, are limited by their invasiveness, high cost, and restricted accessibility. In contrast, ultrasensitive blood-based assays overcome these limitations while offering higher patient compliance, making them increasingly attractive. In recent years, substantial progress has been made in identifying blood biomarkers of AD, yet systematic overviews linking these biomarkers to ultrasensitive detection strategies remain limited. To address this gap, this review first discusses the relevance of blood tests to AD pathophysiology and the underlying disease mechanisms, and summarizes blood biomarkers within the established A/T/N framework. We then comprehensively discuss emerging ultrasensitive detection platforms, including single-molecule immunoassay (SiMoA), mass spectrometry, electrochemical biosensors, nucleic acid amplification techniques, surface plasmon resonance (SPR), and optical spectroscopy, focusing on their capabilities to enhance sensitivity and specificity in blood-based AD detection. Notably, we discuss ultrasensitive AD detection strategies from the standpoint of materials engineering and device innovation, revealing how nanomaterials and functional interfaces improve assay performance, thereby addressing a gap not covered by prior clinical or conventional biochemical studies.

Ubiquitination, a post-translational modification, is a critical regulator of intracellular protein function. Ubiquitination modulates protein functions by promoting proteasomal degradation or altering subcellular localization through ubiquitin chain-dependent signaling. Here, we describe two cell lysis methods for detecting SMAD2 ubiquitination levels in HEK293T cells and compare their effectiveness in analyzing protein ubiquitination levels. Protein overexpression in cells was induced by transient transfection. The plasmids (HA-Ub, FLAG-SMAD2, and MYC-SMURF2) and transfection reagents were separately added to basal medium, mixed, and the mixture was added to the cells. Prior to harvest, MG132 was added to inhibit proteasomal degradation and enhance ubiquitinated protein accumulation. The primary divergence between the two experimental approaches is their cell lysis methods-the ice-bath method (performed at 4 °C) and the heat-treatment method (involving incubation at 95 °C), which substantially affects the efficiency of protein lysis. After cell lysis was completed, the cell lysate, agarose beads, and FLAG antibody were mixed and incubated at 4 °C overnight. Ubiquitinated proteins were then detected by western blot analysis. Before detecting ubiquitinated proteins, a light-chain antibody was used for secondary antibody incubation. Then, ubiquitination bands were detected. The results show that both the ice-bath method and the heat-treatment method can be used to detect ubiquitination levels, while the heat-treatment method may make it easier to detect ubiquitination of SMAD2. This study delineates and compares two cell lysis methods for measuring ubiquitination levels in mammalian cells, using SMURF2/SMAD2 as a model, to assist researchers in selecting more appropriate methods for detecting the ubiquitination levels of substrate proteins.

Gastric anastomotic leakage is a life-threatening postoperative complication that necessitates early detection and timely intervention. Implantable electronics offer direct access to deep tissue and enable accurate recognition of homeostatic imbalances yet are constrained by miniaturization, biodegradability, and power sustainability. Here, we propose an implantable-wearable integrated strategy for early detection of gastric anastomotic leakage. Specifically, a bioresorbable hydrogel patch serves as the sole implant, responding to gastric acid to modulate ultrasound echo signals in both time and frequency domains. This enables early leakage detection (within 1 hour) and millimeter-level spatial localization via a wearable ultrasound array. Meanwhile, the hydrogel patch exhibits acid-enhanced leak sealing capability and degrades naturally after the healing period, thereby eliminating the need for surgical removal. We confirmed its capabilities in spatiotemporal monitoring, anastomotic repair, and bioabsorbability in a rat gastric perforation model. Our system operates without complex electronic implants, highlighting its clinical potential for postoperative monitoring and management of gastric anastomotic leaks.

Toxoplasmosis is a widely distributed zoonosis caused by the protozoan parasite Toxoplasma gondii. Infection during pregnancy is a major public health concern due to its potential impact on both maternal health and fetal development. Early detection of maternal infection is critical to prevent adverse outcomes; however, maternal signs are often subtle, non-specific or absent, complicating timely diagnosis. This scoping review aimed to map and synthesise existing evidence on early maternal signs, pregnancy and foetal outcomes, frequently assessed risk factors, and diagnostic approaches of toxoplasmosis in expectant mothers in Africa. The review was done in accordance with the PRISMA-ScR guidelines. A literature search of PubMed, Scopus, ResearchGate, and Google Scholar was performed to identify studies published between 2000 and 2025. Retrieved records were managed using Zotero (version 8.0.4) for deduplication and screening. Only English-language studies conducted in Africa and reporting relevant maternal or clinical data were included. A total of 28 cross-sectional studies were included. Lymphadenopathy (25.0%) was the most frequently reported maternal early sign, followed by flu-like illness, asymptomatic infection, low-grade or mild fever, and fatigue or malaise (each 10.7%). Congenital anomalies (50.0%) and miscarriage or spontaneous abortion (42.9%) were the most commonly reported foetal and pregnancy outcomes. Frequently reported risk factors were exposure to cat faeces (57.1%) and ingestion of undercooked or raw meat (42.9%). Diagnostic approaches were commonly enzyme-based immunoassays (78.6%), with limited use of RDTs and molecular methods. These findings suggest the need for improved early detection and prevention strategies in high-risk, low-resource African settings. Enhancing routine screening, health education, and access to appropriate diagnostics are considered. Future studies should consider adopting standardised reporting and integrating sensitive, affordable, rapid diagnostic approaches to enhance early detection and reduce the burden of congenital toxoplasmosis.