Browse Articles

Proteotoxic stress response drives T cell exhaustion and immune evasion

Thermal transport mechanisms in ZIFs

Abstract Zeolitic imidazolate frameworks (ZIFs), a subclass of metal-organic frameworks (MOFs), exhibit tunable thermal conductivity, which is crucial for applications such as gas adsorption, catalysis, and energy storage. Despite its significance, thermal conductivity in MOFs remains less explored compared to other key performance indicators. In this work, we investigate the thermal transport properties of 196 ZIF structures with diverse net topologies and organic linkers. We develop a thermal circuit model that quantitatively integrates network topology and consolidates various atomic contributions into heat conduction units for thermal analysis. Our results reveal a strong correlation between circuit-estimated and simulated thermal conductivity, demonstrating the model’s predictive power. Additionally, we find that functional groups influence thermal transport through a competing interplay between atomic mass and mechanical stability. These findings provide a systematic approach for tailoring MOF thermal properties, offering insights into the rational design of materials with optimized thermal performance.

Effects of telitacicept and belimumab on systemic lupus erythematosus: a systematic review and meta-analysis

Abstract B cell-targeted therapies play an important role in systemic lupus erythematosus (SLE). Belimumab targets B lymphocyte stimulator (BLyS), whereas telitacicept inhibits both BLyS and APRIL. Although both drugs have demonstrated clinical efficacy in SLE, comparative benefits and risks of telitacicept versus belimumab remain unclear. We performed a systematic review and meta-analysis using indirect comparisons to compare their efficacy and safety. We searched 6 database for randomized controlled trials (RCTs) published up to November 1, 2025, without language restriction. Trials evaluating belimumab or telitacicept in adult SLE patients were included. Primary outcomes included SLE Responder Index 4 (SRI4), SRI7 response rates and decreasing SLEDAI score. Secondary outcomes included prednisone dose reduction, anti-dsDNA change, adverse events (AEs) and serious AEs. Data extraction and risk-of-bias assessment were performed independently by two reviewers. Analysis used RR with 95% CI, and heterogeneity was assessed by I 2 . Sensitivity, subgroup and publication-bias analyses were performed. 11 trials with telitacicept and belimumab involving 4303 participants were included. Compared with the belimumab group, telitacicept significantly increased the SRI4 response rate (relative risk [RR], 2.03, 95%CI, 1.65–2.49, p  < 0.0001), SRI7 response rate (RR, 3.61, 95%CI, 1.57–8.29, p  = 0.002) and decreased SLEDAI score (RR, 1.67, 95%CI, 1.41–1.97, p  < 0.0001). Compared with belimumab, telitacicept exhibited a significant advantage in SRI4 response rate (p for interaction = 0.0002), without a significant difference in adverse events. Certainty of evidence ranged from moderate to high, but heterogeneity was present for some outcomes. Telitacicept improved SRI4 and SRI7 response rates, reducing disease activity and prednisone dosage, without a clear increase in infection risk compared with belimumab. Dual inhibition of BLyS and APRIL by telitacicept may offer an effective option for reducing SLE activity. Further large-scale, long-term head-to-head trials are needed to confirm these findings.

A unified AI-driven framework for quantum-secured 6G THz networks with intelligent reflecting surfaces and federated edge learning

Abstract The main contribution of this manuscript is an innovative framework for integrating Artificial Intelligence (AI) in 6G wireless systems. With increased complexity, including bursty traffic, network complexity, and dynamic variability, there is a need for intelligence. This study develops and validates an AI-driven approach that enhances network performance through quantum communication decoding, beamforming, and decentralized edge processing. Kalman filtering predictive models are used to estimate variable channel conditions in a Terahertz (THz) network to support beamforming to optimize beamforming. Artificial Intelligence exploits smart reflective surfaces (IRS) strengthening signals and improving their coverage. Also, strong security of Quantum Key Distribution (QKD) protocols due to AI enhanced error correction technology, and rapid, yet privacy information conducting at edge nodes due to decentralised processing through federated learning are examples of enhanced capabilities. Extensive ns-3 simulations across 100 independent runs validate the framework’s effectiveness and prove the system in practical 6G deployment scenarios including THz links, IRS component and edge nodes. The simulation results demonstrate that the proposed framework achieves superior performance compared to conventional approaches, with statistical validation across multiple deployment scenarios. The system decreases latency by 30%, and adds 25% to spectral efficiency. In bursty traffic, the energy efficiency is increased by 20% and packets delivery ratio (PDR) is boosted by 15%. The AI algorithms work effectively to regulate the channel estimation, beamforming, and resource allocation, and, as a result, showed an improvement in the order of magnitudes over previous studies. These results support the fact that AI demonstrates significant potential for transformative impact to a 6G network. The framework has been efficient in addressing problems of channel estimation, beamforming and distributed processing and novel calculations in quantum communication security protocols. Such findings can be used as the foundation of the further inclusion of AI-based technologies in 6G systems, which will help to deploy robust, resilient, and autonomous wireless networks to address the needs of a connective society.

Risk factors for mortality in patients with kidney failure on hemodialysis identified by proteomic analysis of CRIC and PACE studies

Multi-object sperm detection and tracking based on enhanced YOLOv4 and improved DeepSORT

Molecular recognition of natural compounds by MR1 and their implication in MAIT cell activation elucidated through McMD-based dynamic docking simulations

Effect of the S2’ site cleavage on SARS-CoV-2 spike

Impact of treatment modality on survival in FIGO stage IIB cervical cancer: a population-based propensity-score matched analysis

Hybrid fuzzy-MPC based multi-objective control strategy for fast charging of electric vehicles with advanced battery thermal management and renewable grid support

A scalable and long-cycle-life 600 Wh kg−1 solid-state lithium metal pouch cell

Geological conditions for shale gas accumulation and favorable zone evaluation in the Longmaxi formation of the Yanyuan Basin

Advanced fault diagnosis in milling cutting tools using vision transformers with semi-supervised learning and uncertainty quantification

Controlling pyramidal nitrogen chirality by asymmetric organocatalysis

Reduced Cas9 transgene silencing by incorporation of intron sequences

Prognostic value of stromal CD39+CD8+ T cells in predicting platinum sensitivity and survival outcomes in epithelial ovarian cancer

Service preferences of printing houses as a basis for developing product-service system solutions

Odoribacter splanchnicus rescues aging-related intestinal P-glycoprotein damage via GDP-L-fucose secretion

Abstract Intestinal P-glycoprotein (P-gp/ ABCB1 ) is a key barrier limiting xenobiotic absorption, yet its functional decline with aging is poorly understood. Here, we show that gut microbiota dysbiosis contributes to age-associated P-gp deficiency. Integrated multi-omics analyses of human cohorts and murine models identify Odoribacter splanchnicus ( O. splanchnicus ) as a key commensal species whose depletion impairs intestinal P-gp function. Mechanistically, O. splanchnicus encodes GDP-mannose 4, 6-dehydratase (GMDS) and GDP-L-fucose synthase (TSTA3), enabling microbial biosynthesis of GDP-L-fucose. This metabolite directly promotes phosphorylation of the eukaryotic translation initiation factor 4E (eIF4E) and activates c-Jun-driven ABCB1 expression, thereby restoring xenobiotic efflux. These findings establish a microbiota-metabolite-transporter signaling axis that maintains intestinal detoxification, suggesting that targeting either microbes or metabolites could help prevent adverse drug reactions in older adults.

Sedimentation and drag in drifting macrophytes and plastic objects: a model

Abstract Predicting macroalgal sedimentation and drag sensitivity is essential for ecological and geochemical modeling, and for optimizing seaweed cultivation. However, despite the diversity of macrophyte forms, models incorporating their specific morphology and hydrodynamic effects are largely lacking. To develop a broadly applicable model, we tested whether the drag response of diverse macrophyte morphologies and plastic objects can be accurately predicted by approximating them as ellipsoids and accounting for their specific shapes. A set of simple shape descriptors (wet weight, volume, thallus thickness, thallus projection area) and an empirical solution to the drag equation enabled relatively accurate predictions of the sinking velocity for 26 morphologically diverse species of macroalgae in still water, as well as for eelgrass ( Zostera marina ), another major source of drifting biomass in many shallow seas. Additionally, we identified a second simpler empirical solution that incorporates shape and, while slightly less accurate, can be applied to a broader range of particles, including plastics.

Laser diagnostic investigation on flame-assisted spray synthesis of NMC811 battery materials

Abstract There is significant interest in the development of robust and simplified processes for the production of battery materials, including Li(Ni $$_{0.8}$$ Mn $$_{0.1}$$ Co $$_{0.1}$$ )O $$_2$$ (NMC811), which has demonstrated high energy capacity, thermal stability and excellent electrical conductivity. This study developed a flat-flame reactor to provide a flame environment for flame spray pyrolysis (FSP), starting from an aerosolised solution of a mixture of metal nitrates in water. Preliminary studies were conducted to confirm that the operating conditions produced suitable NMC materials with acceptable performance after annealing at 750 $$^{\circ }$$ C. Multiple laser diagnostic techniques were applied to characterise the spatial distribution of reactants and products and capture the process of reaction. Phase Doppler particle analysis was used to capture the droplet characteristics of precursors, and Mie scattering was used to map the instantaneous spatial distribution of droplets. A range of excitation wavelengths was tested to detect the participating species. However, only the high-energy wavelengths below 400 nm were capable of eliciting any signal. Light from a 355 nm pulsed laser was used to excite phase-selective laser-induced breakdown spectroscopy (PS-LIBS) to characterise the spatial distribution of the synthesised particles arising from mixing and reaction in the high temperature zone. The excited emission from the reaction zone was spectrally characterised, as was the corresponding time signature. Finally, simultaneous Mie scattering and PS-LIBS images were obtained to capture both droplet and synthesised particle distribution, capturing the emerging reaction process. The studies show, for the first time, how emissions from the formed particles can be used as a surrogate for the progress of reaction in similar FSP systems.