Background/Objectives: Current management of Hereditary Angioedema (HAE) predominantly focuses on acute attack control and prophylaxis. However, the cumulative "inter-attack" burden driven by chronic low-grade inflammation and its impact on physical frailty remain under-investigated. This preliminary, proof-of-concept study proposes a novel composite score, the "Hereditary Angioedema Frailty and Inflammation Score" (HAE-FIS), to quantify this inter-attack frailty-like inflammatory burden. Methods: In this single-center retrospective study, 46 patients with C1-inhibitor deficiency were evaluated. The HAE-FIS (range 0-5) was constructed using five routinely available biomarkers reflecting inflammation and nutritional status: C-reactive protein (CRP), albumin, hemoglobin, body mass index (BMI), and inter-attack symptom burden. Patients were categorized based on annual attack frequency (Infrequent ≤ 6 vs. Frequent > 6 attacks/year). Results: The median diagnostic delay was 12.0 years. Patients with frequent attacks had significantly higher median HAE-FIS scores compared to the infrequent group (p = 0.008). Component analysis revealed that 'High CRP' (46.4% vs. 16.7%; p = 0.039) and 'Low BMI' (25.0% vs. 0.0%; p = 0.032) were significantly more prevalent in patients with frequent attacks. Notably, low BMI was observed exclusively in the frequent attack group, suggesting a specific phenotype at risk for sarcopenia. While multivariable logistic regression was constrained by the sample size, the annual attack count emerged as the strongest predictor for a high HAE-FIS score (OR: 1.049; p = 0.070). Conclusions: High attack frequency in HAE is associated with a measurable cumulative systemic burden characterized by inflammation and nutritional risk. These findings support the development of an "HAE Rehabilitation" framework integrating functional preservation into long-term management.
Predator-prey interactions can drive arms races of complex adaptation and response patterns. While collective behaviour is common as anti-predator behaviour and well understood mechanistically, little is known on predator strategies in the context of collective prey defences. Here, we investigated attack strategies of predatory birds confronted with an effective collective anti-predator behaviour employed by shoals of small freshwater fish (Poecilia sulphuraria). Analysing over 700 attacks revealed that predators face a trade-off: placing attacks in the shoal centre leads to a stronger prey response, which decreases attack frequency but increases chance for success. The causal relationship between attack location and prey-response plasticity was confirmed experimentally through simulated attacks. Predators adjusted their strategies with conspicuous species avoiding the centre, sacrificing optimal attack positions for lower prey responses. A cryptic predator causing overall low-intensity responses favoured central attacks, showing that the magnitude of the prey's defence is driving predator attack strategies. Additionally, we report priming effects where repeated attacks in spatial proximity intensified the prey's response, leading to predator species spacing attacks farther apart, suggesting priming counters sequential hunting strategies. We highlight the complex behavioural patterns underlying predator-prey dynamics in the wild, while mechanistic aspects of collective priming prompt interesting future research directions.
DNNs are highly vulnerable to adversarial examples (AEs). To achieve high transferability, traditional AEs often introduce unnatural artifacts that are easily perceptible to the human eye. Unrestricted attacks have emerged as a promising paradigm to generate more natural unrestricted adversarial examples (UAEs). However, existing UAEs struggle to balance visual fidelity and black-box transferability. Color-based attacks produce noticeable unnatural visual mutations, and diffusion-based attacks transfer poorly to unknown black-box models. We observe that directly injecting unconstrained random perturbations into the diffusion latent space destroys the normal distribution of data, thereby causing a distribution shift. Distribution shifts degrade adversarial perturbations into invalid noise and cause surrogate model overfitting. Furthermore, introducing elastic deformation during the denoising process forces surrogate models to focus on highly transferable features. As a result, we propose an unrestricted attack based on deformation-constrained diffusion, called DeDiAttack. Our method utilizes the manifold prior knowledge of diffusion models to translate elastic deformations into smooth fluid changes. The mechanism effectively eliminates unnatural artifacts and generates highly natural and transferable UAEs. Extensive black-box experiments demonstrate that DeDiAttack outperforms existing attacks and improves the black-box transferability of generated UAEs by 7.2% on the ViT-B surrogate model. The proposed method also provides a useful robustness evaluation tool for vision-based sensing and imaging systems.
Eosinophilic airway inflammation predicts asthma attacks and inhaled corticosteroid (ICS) response in adults; similar mechanisms may apply to preschool wheeze. This study assessed whether blood eosinophil count (BEC) alone or combined with allergic sensitisation and fractional exhaled nitric oxide (F ENO) was associated with future wheeze attacks. 95 preschool children (12-59 months old) with clinician-confirmed wheeze were recruited from primary and secondary care. At baseline, finger-prick BEC, skin-prick testing for allergic sensitisation and offline F ENO were performed. Children were followed for 8-9 months. The primary outcome was the number of acute wheeze attacks diagnosed during unscheduled visits to an emergency department or general practitioner, documented by parental reports, medical records or oral corticosteroid prescriptions. Exploratory analyses examined ICS association with wheeze attack odds across different biomarker subgroups. Children with BEC ≥300 cells·μL-1 had higher wheeze attack odds over 9 months (n=60, odds ratio (OR) 4.27, 95% confidence interval (CI) 1.7-11.38). Odds were greatest in those with BEC ≥300 cells·μL-1 and F ENO ≥10 ppb (n=12, OR 60.74, 95% CI 2.98-1238.9). ICS prescription was associated with reduced 3-month wheeze attack odds among children with elevated BEC (n=21, OR 0.11, 95% CI 0.02-0.49) or allergic sensitisation (n=19, OR 0.11, 95% CI 0.01-0.65), with further reduction when both were combined (n=10, OR 0.06, 95% CI 0.002-0.59). Elevated BEC may identify preschool children at increased wheeze attack odds, particularly when combined with F ENO. ICS treatment was associated with odds reduction in children with elevated BEC or allergic sensitisation. These findings provide a rationale for future randomised controlled trials comparing biomarker-guided and symptom-based treatment strategies.
The global navigation satellite system (GNSS), the main time synchronization method for phasor measurement units (PMUs) in smart grids, is highly vulnerable to time synchronization attacks (TSAs). This affects the timing of results and poses a serious threat to the safe and stable operation of power systems. To quickly detect TSAs and minimize the impact of time errors on PMU sensor networks, a TSA detection method based on carrier Doppler Pearson correlation coefficient estimation is proposed. This method can be directly implemented on existing commercial receivers without modifications. The method leverages the fact that carrier Doppler shifts in each satellite channel exhibit consistent changes when subjected to a TSA; therefore, if there is a correlation between channels, a consistent change in carrier Doppler shift caused by the TSA can be quickly detected through Pearson correlation coefficient estimation. In the TSA detection experiment, the proposed method was compared against four existing TSA detection methods on a self-developed experimental platform. The experimental results show that compared with the other four methods, the proposed method responds 4-22 s faster and has better detection speed, with more significant changes in the detection statistics. Notably, these advantages become more pronounced as the spoofing speed decreases and the spoofing stealthiness increases, indicating that this method has robust detection capability against sophisticated attacks. Meanwhile, it offers a lightweight computational overhead suitable for embedded PMU implementations, enhancing sensor-layer security in critical infrastructure. This work provides reliable synchronized measurements for power system monitoring and control over a wide area.
Thaumasite sulfate attack (TSA) under elevated water pressure has important implications for the durability of deep underground concrete structures, yet the deterioration process and the coupled effect of water pressure and carbonate supply remain insufficiently understood. In this study, laboratory pressurized sulfate exposure tests were conducted to investigate the evolution of macroscopic performance and microstructure of cement mortars with different limestone powder contents (0%, 15%, and 30%) under water pressures of 0, 2.5, and 5.0 MPa. The results show that elevated water pressure promotes sulfate ingress into the mortar and accelerates later-stage strength loss; this interpretation is supported by the depth-dependent distribution of soluble SO42- measured in mortars without limestone powder. Two-way ANOVA indicates that both water pressure and limestone powder content have significant effects on compressive strength, and their interaction becomes statistically significant at 120 d. XRD, FT-IR, and SEM/EDS results show that, under elevated water pressure and high limestone powder content, the corrosion products gradually evolve from gypsum-related products to ettringite- and thaumasite-related products, with a certain spatial differentiation. Specifically, the gray-white, mud-like surface products are consistent with thaumasite-rich assemblages, whereas the needle- and column-like crystals in the interior are consistent with ettringite-rich assemblages. Overall, elevated water pressure mainly promotes sulfate transport, while limestone powder mainly increases carbonate availability. These two factors may jointly intensify TSA deterioration in mortar through a pathway involving transport enhancement, carbonate supply, corrosion product evolution, and aggravated macroscopic damage. This study provides a reference for understanding the sulfate deterioration mechanism of limestone powder-containing cement-based materials in deep underground environments under elevated water pressure.
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To define incident heart failure (HF) risk in ischaemic stroke/TIA survivors. The secondary aims were to define the association of HF with all-cause mortality in stroke survivors and to describe their cardiac magnetic resonance (CMR) findings. This was a prospective cohort study using the UK Biobank (UKBB) cohort of individuals aged 40 to 69-years-old. We excluded individuals with prior HF and stratified them by history of ischaemic stroke/TIA. The main outcome was incident HF as defined by hospital admissions coded for heart failure. Secondary outcomes were all-cause mortality, myocardial infarction and CMR findings. We included 405,406 individuals (age 56.5 years, 45.6% males). Over 13.7 years, 15,565 individuals experienced incident HF. Stroke survivors had an overall HR of 3.6 (95% confidence interval 3.3 to 3.8, p<0.0001) for HF hospitalization and an adjusted HR of 1.4 (95% confidence interval 1.3 to 1.5, p<0.0001). The risk of HF hospitalization was greater than the risk of myocardial infarction (12.6% vs 5.4%). Stroke survivors with HF had a lower LVEF and higher LV mass than those without HF. Incident HF in stroke survivors was associated with a HR of 1.8 (95% confidence interval 1.6 to 1.9, p<0.0001) for mortality. Incident HF is common in stroke survivors and strongly associates with mortality. The risk of HF varies greatly depending on underlying risk factors. Exploratory analyses suggest that stroke survivors with HF may have a lower ejection fraction phenotype. Future trials of HF preventive therapy in high-risk stroke survivors are warranted.
Bacterial pathogens must overcome oxidative stress to survive within host phagocytes. Although canonical systems such as OxyR are well characterized, alternative pathways remain poorly understood. Here, we identified YchJ, a conserved yet uncharacterized protein, as a central redox-sensitive transcription factor that coordinates a major antioxidant defense system in Salmonella independently of OxyR. Deletion of ychJ severely impaired bacterial survival under H₂O₂ stress and within macrophages. Proteomic analysis revealed that YchJ represses rssB, leading to RpoS accumulation and upregulation of key antioxidant enzymes, including SodC and KatE. Our results show that YchJ directly binds the rssB promoter as a transcription factor. Structural analysis revealed that ROS sensing by YchJ is achieved through reversible dimerization mediated by an intermolecular disulfide bond. This conformational switch enables a C-terminal basic-rich region of the dimer to recognize a palindromic sequence in the rssB promoter and repress rssB transcription. Dual-transcriptome analysis further confirmed that YchJ directly activates antioxidant defenses in Salmonella and significantly disrupts host pathways during intramacrophage infection. Our findings elucidate a previously unrecognized redox-sensing pathway essential for bacterial virulence and uncover a transcriptional mechanism controlling RpoS stability, thereby expanding our understanding of the stress-response regulation system in pathogenic bacteria.
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Adversarial attack strategies for 3D object detection have highlighted the critical importance of addressing security concerns in this domain. However, white-box methods require full access to the victim model in large-scale point cloud applications. To this end, we propose a novel Policy-Driven Black-box Attack (BAT) that is designed to optimize attack locations without necessitating detailed knowledge of the victim models. First, we introduce a density-aware pattern generator that creates scene-adaptive attack clusters. Second, we leverage the deep deterministic policy gradient in deep reinforcement learning to train an attack agent capable of targeting the victim model. Ultimately, the attack agent is iteratively directed towards optimal attack locations through the joint application of critic loss and actor loss. To the best of our knowledge, this represents the first reinforcement learning-based black-box attack applied to practical 3D object detection. Experimental results on the KITTI, nuScenes, and Waymo datasets demonstrate that BAT effectively diminishes the accuracy of notable models. Importantly, BAT significantly enhances the attack success rate (surpassing state-of-the-art both white-box and black-box methods) and increases transferability (by 20 times) through simple deep deterministic policy gradient, thus establishing a new baseline for adversarial attacks in 3D object detection.
Software-defined wide area networks (SD-WAN), empowered by software-defined networking (SDN) technology, offer unparalleled flexibility and efficiency in wireless communication. However, their integration introduces new security challenges, particularly in mitigating distributed denial of service (DDoS) attacks. In this paper, we propose an advanced security framework tailored to SDN-enabled SD-WAN. A dataset collected during experiments was used for training and testing the model's performance. Our framework leverages machine learning algorithms to detect and classify DDoS attacks targeting SD-WAN controllers, considering the environment's unique characteristics. We develop adaptive machine learning model capable of accurately discerning high-rate and low-rate DDoS attacks, enhancing the network's resilience against sophisticated threats. Results from controlled experiments show promise for real-world deployment, though further validation is needed. Our results highlight the framework's ability to adapt to dynamic network conditions and provide robust security for SDN-enabled SD-WAN. Our adaptive model integrates RF and DT explicitly for SD-WAN contexts, achieving 99.97% accuracy for high-rate attacks and 99.96% for low-rate attacks. Our QT-PCA preprocessing pipeline reduces dimensionality while preserving performance, and PACKET_IN event-triggered mitigation enables dynamic response. This method significantly enhances security solutions' accuracy, ensuring robust DDoS attack detection in SD-WAN environments. Additionally, by leveraging the SD-WAN architecture's efficiency, our approach optimizes network performance, underscoring its efficacy and practicality in enhancing security and efficiency.
Quantifying the impacts of armed conflict on civilians and infrastructure remains a major challenge, particularly where reporting is limited. Most conflict measurement tools require affected populations to report events and are limited by short time series, under-reporting, and varying methods. These tools do not capture infrastructural rebuilding, which has important health implications. Given this, we demonstrate the utility of nighttime lights (NTL) as a complementary tool for measuring conflict dynamics and infrastructure recovery with an epidemiological application. We used monthly NASA Black Marble data to analyze NTL patterns in Yemen (2012-2022) and Ukraine (2019-2024) before and after the onset of large-scale military operations. We calculated month-specific NTL ratios relative to pre-event baselines and assessed the alignment of structural breakpoints, identified using BFAST methods, with aerial attack onset. Generalized additive models were used to measure the relationship between NTL and aerial attacks while accounting for the built environment, population, diesel price (Yemen), and spatiotemporal factors. Finally, we applied NTL to an existing model on the association between conflict, measured via air raids, and cholera in Yemen by replacing the original conflict categories with ones defined by NTL and included a variable for NTL recovery. Mean NTL declined by 53.3% in Yemen and 21.0% in Ukraine following conflict escalation, with detected breakpoints aligning with aerial attack onset in 85.7% of Yemeni governorates and 51.9% of Ukrainian oblasts. Generalized additive models showed that attacks were significantly associated with NTL reductions, independent of built environment factors. Incorporating NTL-based conflict measures into a cholera transmission model for Yemen produced results consistent with attack-based models and found that light recovery was associated with reduced disease risk. NTL is a viable tool for measuring conflict and can offer insights on dynamics that are not present in standard tools while avoiding many of these tools' limitations. These data have epidemiological applications and can be a proxy for important events affecting transmission dynamics. While event-based tools have vast utility, NTL can complement them with specific strengths and means of application.
Automated mobile microscopy in Internet of Things (IoT) networks is essential for scaling malaria screening in resource-constrained environments. Deploying standard convolutional architectures here introduces severe adversarial vulnerabilities. Post-Training Quantization (PTQ) mitigates hardware constraints by converting floating-point models to 8-bit integers (INT8); however, its impact on clinical safety and security remains unexplored. This study presents an adversarial audit of quantized Vision Transformers for medical edge deployment. We evaluated a Swin-Tiny transformer against ViT-Tiny and MobileNetV3 baselines using a 27,558-image malaria dataset and an out-of-distribution (OOD) White Blood Cell dataset. Our findings redefine the "Quantization Shield" hypothesis. PTQ compresses the Swin model by 3.9× (to 27.89 MB) with a negligible 0.11% accuracy drop, maintaining statistical reliability on OOD tests. However, the hypothesized architectural resilience shatters under white-box Projected Gradient Descent (PGD) attacks. Despite robustness against single-step attacks, both MobileNetV3 and the INT8 Swin-Tiny collapse to 0.00% accuracy under iterative PGD. Conversely, the quantized Swin-Tiny resists black-box transfer attacks from a surrogate, maintaining 81.00% accuracy. We conclude that while quantized Vision Transformers meet mobile sensor constraints, integer quantization provides zero innate defense against targeted iterative perturbations, exposing a critical vulnerability in diagnostic IoT networks.
The occurrence of dyspnea in patients with acute coronary syndrome (ACS) has always been considered a challenging diagnostic and therapeutic clinical scenario. Ticagrelor, a platelet receptor inhibitor, has beneficial effects in the prevention of ischemic events and mortality, but can cause dyspnea in ACS patients. In this study, we intend to investigate the prevalence of dyspnea in patients with ACS who are under treatment with ticagrelor. This prospective observational cohort study included 200 patients diagnosed with ACS from March 2020 to March 2022 and referred to the Cardiology Department of Modarres Hospital. After prescribing the medicine, the presence and severity of dyspnea were recorded daily according to the modified Borg dyspnea scale during hospitalization. Patients were followed up for 3 months after discharge, and the occurrence of dyspnea was investigated based on the characteristics of the patients. There were no major differences between groups in terms of gender, diabetes, hypertension, or smoking status. Most patients had normal lung auscultation, and the majority presented with unstable angina. Our results showed that 21.5% of patients experienced dyspnea, and 3% of patients stopped using drugs due to the severity of dyspnea. The time of the first attack varied from 1.5 to 36 hours after drug administration, and the maximum intensity of the first attack reached 7. The duration of the second attack varied from 2 to 22 days and was observed with a maximum intensity of 6. The age of patients was significantly higher in people who experienced dyspnea compared to those without dyspnea. Dyspnea is a common side effect in ACS patients receiving ticagrelor. The etiology of new-onset dyspnea in these patients can be complex and challenging to evaluate, as conventional factors did not appear to predict its occurrence. Given the significant therapeutic benefits of ticagrelor, discontinuation should only be considered for persistent and intolerable dyspnea associated with the medication. Further large-scale studies are warranted to better understand its clinical significance and to develop optimal management strategies.
The Industrial Internet of Things (IIoT) has become a cornerstone of modern industrial automation, enabling real-time monitoring, intelligent decision-making, and large-scale connectivity across cyber-physical systems. However, the growing scale, heterogeneity, and dynamic behavior of IIoT environments significantly expand the attack surface and challenge the effectiveness of conventional security mechanisms. In this paper, we propose DeepSense, a hybrid and adaptive anomaly and intrusion detection framework specifically designed for resource-constrained and heterogeneous IIoT deployments. DeepSense integrates three complementary components: DataSense, a realistic data pipeline and experimental testbed supporting synchronized sensor and network data processing; RuleSense, a lightweight rule-based detection layer that provides fast, deterministic, and interpretable anomaly screening at the edge; and NeuroSense, a learning-driven detection module comprising an adaptive ensemble of 22 machine learning and deep learning models spanning classical, neural, hybrid, and Transformer-based architectures. NeuroSense operates as a second detection stage that validates suspicious events flagged by RuleSense and enables both coarse-grained and fine-grained attack classification. To support rigorous and practical assessment, this work further introduces a comprehensive performance evaluation framework that extends beyond accuracy-centric metrics by jointly considering detection quality, latency, resource efficiency, and detection coverage, alongside an optimization-based process for selecting Pareto-optimal model ensembles under realistic IIoT constraints. Extensive experiments across diverse detection scenarios demonstrate that DeepSense exhibits strong generalization, lower false positive rates, and robust performance under evolving attack behaviors. The proposed framework provides a scalable and efficient IIoT security solution that meets the operational requirements of Industry 4.0 and the resilience-oriented objectives of Industry 5.0.
This post hoc analysis examined the impact of prior immunosuppressants on the long-term efficacy and safety of inebilizumab, a cluster of differentiation 19+ B-cell-depleting monoclonal antibody, in participants with aquaporin-4-seropositive neuromyelitis optica spectrum disorder from the N-MOmentum trial (NTC02200770). Inebilizumab treatment resulted in a sustained low annualized attack rate relative to the pretrial annualized attack rate and a high probability of remaining attack-free for up to 4 years among participants with and without prior immunosuppressant use. Based on modeling data, inebilizumab had greater long-term efficacy than historical immunosuppressants. Inebilizumab was well tolerated regardless of prior immunosuppressant use.
The rapid proliferation of the Internet of Things (IoT) leaves terminal devices vulnerable to considerable security challenges, notably the absence of robust yet efficient identity authentication mechanisms. Traditional certificate-based approaches incur substantial management overhead and storage expenditure, whereas Identity-Based Cryptography poses inherent key escrow risks. To tackle these challenges, this paper proposes a PUF and SM2-based certificateless identity authentication mechanism that integrates SM2 Certificateless Public Key Cryptography (a Chinese national cryptographic standard) with Physical Unclonable Functions (PUFs). Initially, the proposed solution utilizes PUF technology to derive a unique hardware-generated "fingerprint" from an IoT device, which functions as a root key to generate a partial user private key. This approach essentially binds the terminal's identity to its physical hardware, thereby effectively mitigating physical cloning attacks against nodes. Moreover, through the adoption of a Certificateless Public Key Cryptography (CLPKC) framework, the complete user private key is jointly generated by a semi-trusted Key Generation Centre (KGC) and the terminal device itself. The comprehensive security analysis proves that the proposed scheme is provably secure under the random oracle model, capable of resisting various common attacks such as physical cloning, man-in-the-middle, and replay attacks. Performance evaluation confirms that the implemented PUF + SM2 certificateless mechanism significantly reduces the size of user public key identifiers to within 64 bytes, offering a substantial advantage over the 1-2 KB certificates typically required in conventional PKI/CA systems, thereby enhancing efficiency in storage and communication.
Targeting cloud computing environments has become increasingly attractive to sophisticated cyberattackers due to their open, scalable, and distributed nature. Intrusion Detection Systems (IDSs) analyse traffic patterns to detect these attacks; deep learning has become a common approach for building such systems, but many suffer from overfitting, high false-positive rates, and/or unstable training behaviour. To overcome these drawbacks, this paper introduces a Taylor Wave Search Algorithm-optimised Wide Residual Network (TaWSA_WRN) framework for intrusion detection and mitigation in cloud environments, which protects against incoming attacks. The network traffic data accessed from benchmark datasets, such as NSL-KDD and CICIDS2017, are subsequently preprocessed by restoring missing values and applying Min-Max normalisation. Finally, the TaWSA_WRN model performs feature selection and intrusion classification, using the Taylor Wave Search Algorithm to enhance parameter optimisation and improve learning stability by optimising a hyperparameter. This model-agnostic interpretability approach, using SHAP, also offers valuable insights into domain-relevant traffic features. The empirical results show that the proposed approach can achieve maximum TNR, accuracy, and TPR of 96.857%, 97.190%, and 97.589%, respectively. We introduce the mitigation component as a response-guided strategy based on detections rather than a single network-level defence. The proposed framework enables greater reliability, interpretability, and robustness, thereby facilitating detection in a secure cloud computing environment. Code implementation is available at https://github.com/vedasahithi/wideResNet-for-intrusion-detection-.
Cluster headache (CH) is a severe unilateral trigeminal autonomic cephalalgia with limited well tolerated therapies. Calcitonin gene related peptide monoclonal antibodies, including galcanezumab, fremanezumab, and eptinezumab, have established efficacy in migraine and are under evaluation in CH. This study assesses their efficacy and safety using pairwise and network meta analysis. PubMed, Embase, Scopus, and ClinicalTrials.gov were searched through April 2025. Randomized controlled trials enrolling adults with episodic or chronic CH were included. The primary endpoint was change in weekly attack frequency. Secondary outcomes included subtype specific effects and adverse events. Frequentist random effects network meta analysis and pairwise meta analysis were performed in R. Five trials with approximately 1,000 participants were included. No agent demonstrated statistically significant reduction in weekly attack frequency versus placebo, although consistent numerical improvements were observed. Dose specific network estimates showed mean differences of - 0.81 for galcanezumab 300 mg, - 0.17 for eptinezumab 400 mg, 0.71 for fremanezumab 675/225 mg, and - 1.27 for fremanezumab 900/225 mg. Pooled dose estimates were - 0.81 for galcanezumab, - 0.27 for fremanezumab, and - 0.17 for eptinezumab. Pairwise meta analysis yielded a pooled mean difference of - 0.41. Subgroup analysis indicated minimal change in chronic CH and greater numerical reduction in episodic CH. CGRP monoclonal antibodies demonstrate modest directional reductions in attack frequency without statistical significance. Signals appear more pronounced in episodic CH, supporting the need for adequately powered, subtype specific trials.