INTRODUCTION:The COVID-19 pandemic has disproportionately affected immunocompromised individuals, who remain at risk for severe disease despite widespread vaccination efforts. Poor vaccine-induced humoral responses in this population necessitate additional preventive strategies. Sipavibart (AZD3152) is a next-generation long-acting monoclonal antibody designed to target the receptor-binding domain (RBD) of the SARS-CoV-2 Spike protein and provide broad-spectrum neutralization against divergent variants. AREAS COVERED:This review evaluates sipavibart's preclinical pharmacology, pivotal and supportive clinical trial data, and early real-world evidence, including the SUPERNOVA Phase 3 trial and national early-access programs. We discuss its safety profile, variant-specific activity, and resistance challenges. EXPERT OPINION:Sipavibart was the first monoclonal antibody to show efficacy and safety in preventing symptomatic COVID-19 among immunocompromised individuals, protecting for up to six months. However, the widespread circulation of variants harboring S:F456L currently limits its clinical utility, and use should be restricted. Maintaining access to Sipavibart remains justified, as future antigenic shifts could restore its activity. Its deployment should rely on genomic surveillance and local epidemiology. At the same time, next-generation mAbs should prioritize conserved spike regions and multi-epitope cocktails to counter viral evolution and prolong therapeutic value.
In urban and disaster environments, non-line-of-sight (NLoS) blockage and strict end-to-end latency constraints jointly degrade multi-UAV connectivity, especially when stable backhaul is unavailable. We present a RISaugmented distributed crowdsourcing multi-agent reinforcement learning (MARL) framework in which UAV agents and RIS agents are modeled as independent learners under centralized training and distributed execution (CTDE). Each UAV learns motion and single-link selection (direct-only or direct + one selected RIS) with PPO, while each RIS learns a discrete phase/codebook policy with a categorical PPO backend. Our learning objective explicitly internalizes (i) step-wise deadline-exceedance penalties (ReLU of delay above deadline), (ii) a priced bandwidth-sharing budget for control/neighbor messages via an online dual variable, and (iii) a non-negative diversity loss that discourages traffic collapse onto a single RIS. Tail metrics (delay p95/p99) and deadline miss rate (DMR) are used strictly as evaluation KPIs and are not backpropagated through. In simulation under matched urban/disaster settings, we observe that RIS-augmented MARL improves service-level reliability and tail behavior under contention: success rate increases while delay p95 decreases compared with heuristic baselines. The average SNR improvement is modest (e.g., -4.848 dB vs. -4.74 dB under the common logging schema), so our claims focus on tail-aware robustness and deadline feasibility rather than large mean-SNR gains. Overall, these results demonstrate that coupling learnable RIS control with distributed MARL and explicit overhead/deadline pricing yields a practical design point for edge AI-enabled crowdsourcing UAV communications under NLoS and time-critical constraints.
BACKGROUND:Emerging and re-emerging arboviral infections are a risk to blood safety. We conducted an international survey on how blood establishments respond to current and future arbovirus threats. STUDY DESIGN AND METHODS:A questionnaire on arbovirus donor deferral strategies, pathogen reduction, and donation screening was distributed to members of the International Society of Blood Transfusion working party on transfusion-transmitted infectious diseases. Data from 2024 were gathered and analyzed. RESULTS:A total of 23 survey responses were received from 21 countries. This covered a population of 1.45 billion people and 29.9 million blood donations collected in 2024. All respondents applied travel-based donor deferrals, whereas pathogen reduction, implemented by half of the respondents, was mostly applied for a selection of plasma and platelet donations. West Nile virus (WNV) was the only arbovirus blood donations were screened for by nine respondents from eight countries, with 256 donations confirmed as WNV RNA-positive in 2024. No transfusion-transmitted WNV infections were reported. DISCUSSION:Blood safety measures remain limited and unevenly distributed globally, and in their present form, are unlikely to provide protection against the growing range of emerging arboviruses. Donor deferral may not always be a sustainable blood safety strategy alone for all blood operators, due to large-scale outbreaks associated with these viruses. While pathogen reduction methodologies are being developed to be applied to all blood components, risk assessments for (re)-emerging arboviruses, such as dengue, chikungunya, and Zika viruses, should be performed to determine if additional mitigation, such as blood donation screening, is warranted.
Autonomous network management increasingly fuses multiple heterogeneous detectors—such as the intrusion detectors that monitor different traffic planes for 6G and IoT security—through adaptive trust-weighted consensus. When trust is updated online, however, such systems face a fundamental stability-agility trade-off: they are either calm but slow to react to novel threats, or fast but erratic under routine noise. We identify and formalize the resulting failure modes of trust collapse and blind conformity, and propose DELTA (Dynamic Entropy-driven Logistic Trust Aggregation), a self-regulating trust-management framework. DELTA couples a Fixed-Share Redistribution regularizer, which guarantees a minimum trust quota for every detector, with an entropy-amplified logistic controller whose learning rate is driven by the current leader’s error rate and amplified by the ensemble’s structural entropy; this keeps the system quiescent under normal traffic yet triggers a rapid, bounded re-calibration the moment the trusted detector begins to fail. We prove that DELTA enforces a strictly positive diversity floor—making trust collapse provably impossible—and derive bounds on its transition latency and stationary volatility. Across an extensive evaluation—including robustness to delayed, missing, and adversarial feedback, comparison against expert-advice, Bayesian, and change-point baselines with confidence intervals, and validation on the real UNSW-NB15 intrusion dataset—DELTA recovers from zero-day regime shifts where naive baselines collapse below chance, while remaining an order of magnitude more stable than aggressive adaptive methods, all at O(N) computational and communication cost.
