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Tumor drug resistance has been reported to be associated with drug efflux in tumor cells. Recently, a noninvasive and safe mechanism, sonodynamic therapy (SDT), has been proposed to be an oxidative stress strategy to potentially overcome drug efflux, but with efficacy limitation. Herein, we propose a systematic strategy for optimizing SDT, especially revealing the key role of acoustics parameters acting in SDT efficiency. A doxorubicin (DOX)‐loaded sonosensitive micelle (DPM) mediated “sono‐force” combination (chemotherapy and sonodynamic) therapy strategy, named DPCSTs, which was designed for amplifying SDT to augment oxidative stress to overcome drug efflux and induce robust long‐term inhibition of tumor development by optimized acoustic parameters. The sub‐10 nm size DPM enhanced tumor targeting and renal clearance. Meanwhile, another important component, doxorubicin, significantly suppressed residual tumors (78.6%) due to “sono‐force” augmented oxidative stress reversing drug efflux, finally leading to long‐term tumor development limitation in vivo. It is the first time to propose a systematic strategy for optimizing SDT regimens to overcome resistance, which can synergize with chemotherapy to exert long‐term tumor development inhibition. We believe that this work will advance SDT‐related research to a new level, and improve our understanding of overcoming resistance of targeted cancer therapy. The study reveals that optimizing various parameters of sonodynamic therapy (SDT), including the functional characteristics of sonosensitizers and the optimization of acoustic parameters, can significantly enhance SDT therapeutic efficacy. When combined with chemotherapy, this approach effectively overcomes tumor resistance and induces antitumor immune response.

Membrane contact sites (MCSs) are defined as regions where two organelles are closely apposed, and most MCSs associated with each other via protein-protein or protein-lipid interactions. A number of key molecular machinery systems participate in mediating substance exchange and signal transduction, both of which are essential processes in terms of cellular physiology and pathophysiology. The endoplasmic reticulum (ER) is the largest reticulum network within the cell and has extensive communication with other cellular organelles, including the plasma membrane (PM), mitochondria, Golgi, endosomes and lipid droplets (LDs). The contacts and reactions between them are largely mediated by various protein tethers and lipids. Ions, lipids and even proteins can be transported between the ER and neighboring organelles or recruited to the contact site to exert their functions. This review focuses on the key molecules involved in the formation of different contact sites as well as their biological functions.

When cells are stressed, DNA from energy-producing mitochondria can leak out and drive inflammatory immune responses if not cleared. Cells employ a quality control system called autophagy to specifically degrade damaged components. We discovered that mitochondrial transcription factor A (TFAM)—a protein that binds mitochondrial DNA (mtDNA)—helps to eliminate leaked mtDNA by interacting with the autophagy protein LC3 through an autolysosomal pathway (we term this nucleoid-phagy). TFAM contains a molecular zip code called the LC3 interacting region (LIR) motif that enables this binding. Although mutating TFAM’s LIR motif did not affect its normal mitochondrial functions, more mtDNA accumulated in the cell cytoplasm, activating inflammatory signalling pathways. Thus, TFAM mediates autophagic removal of leaked mtDNA to restrict inflammation. Identifying this mechanism advances understanding of how cells exploit autophagy machinery to selectively target and degrade inflammatory mtDNA. These findings could inform research on diseases involving mitochondrial damage and inflammation. Liu, Zhen, Xie, Luo, Zeng, Zhao et al. show that the major nucleoid protein TFAM interacts with cytoplasmic LC3B during oxidative or inflammatory stress to attenuate mitochondrial DNA-induced inflammation via the cGAS–STING pathway.

Cancer cells with RAS mutations exhibit enhanced autophagy, essential for their proliferation and survival, making it a potential target for therapeutic intervention. However, the regulatory differences between RAS-induced autophagy and physiological autophagy remain poorly understood, complicating the development of cancer-specific anti-autophagy treatments. In this study, we identified a form of non-canonical autophagy induced by oncogenic KRAS expression, termed RAS-induced non-canonical autophagy via ATG8ylation (RINCAA). RINCAA involves distinct autophagic factors compared to those in starvation-induced autophagy and incorporates non-autophagic components, resulting in the formation of non-canonical autophagosomes with multivesicular/multilaminar structures labeled by ATG8 family proteins (e.g., LC3 and GABARAP). We have designated these structures as RAS-induced multivesicular/multilaminar bodies of ATG8ylation (RIMMBA). A notable feature of RINCAA is the substitution of the class III PI3K in canonical autophagy with PI4KB in RINCAA. We identified a regulatory P38-ULK1-PI4KB-WIPI2 signaling cascade governing this process, where ULK1 triggers PI4KB phosphorylation at S256 and T263, initiating PI4P production, ATG8ylation, and non-canonical autophagy. Importantly, elevated PI4KB phosphorylation at S256 and T263 was observed in RAS-mutated cancer cells and colorectal cancer specimens. Inhibition of PI4KB S256 and T263 phosphorylation led to a reduction in RINCAA activity and tumor growth in both xenograft and KPC models of pancreatic cancer, suggesting that targeting ULK1-mediated PI4KB phosphorylation could represent a promising therapeutic strategy for RAS-mutated cancers.

ATP9A, a lipid flippase of the class II P4-ATPases, is involved in cellular vesicle trafficking. Its homozygous variants are linked to neurodevelopmental disorders in humans. However, its physiological function, the underlying mechanism as well as its pathophysiological relevance in humans and animals are still largely unknown. Here, we report two independent families in which the nonsense mutations c.433C>T/c.658C>T/c.983G>A (p. Arg145*/p. Arg220*/p. Trp328*) in ATP9A (NM_006045.3) cause autosomal recessive hypotonia, intellectual disability (ID) and attention deficit hyperactivity disorder (ADHD). Atp9a null mice show decreased muscle strength, memory deficits and hyperkinetic movement disorder, recapitulating the symptoms observed in patients. Abnormal neurite morphology and impaired synaptic transmission are found in the primary motor cortex and hippocampus of the Atp9a null mice. ATP9A is also required for maintaining neuronal neurite morphology and the viability of neural cells in vitro. It mainly localizes to endosomes and plays a pivotal role in endosomal recycling pathway by modulating small GTPase RAB5 and RAB11 activation. However, ATP9A pathogenic mutants have aberrant subcellular localization and cause abnormal endosomal recycling. These findings provide strong evidence that ATP9A deficiency leads to neurodevelopmental disorders and synaptic dysfunctions in both humans and mice, and establishes novel regulatory roles for ATP9A in RAB5 and RAB11 activity-dependent endosomal recycling pathway and neurological diseases.

Static analysis is effective for discovering software vulnerabilities but notoriously suffers from incomplete source--sink specifications and excessive false positives (FPs). We present AdaTaint, an LLM-driven taint analysis framework that adaptively infers source/sink specifications and filters spurious alerts through neuro-symbolic reasoning. Unlike LLM-only detectors, AdaTaint grounds model suggestions in program facts and constraint validation, ensuring both adaptability and determinism. We evaluate AdaTaint on Juliet 1.3, SV-COMP-style C benchmarks, and three large real-world projects. Results show that AdaTaint reduces false positives by 43.7\\% on average and improves recall by 11.2\\% compared to state-of-the-art baselines (CodeQL, Joern, and LLM-only pipelines), while maintaining competitive runtime overhead. These findings demonstrate that combining LLM inference with symbolic validation offers a practical path toward more accurate and reliable static vulnerability analysis.

Software fuzzing has become a cornerstone in automated vulnerability discovery, yet existing mutation strategies often lack semantic awareness, leading to redundant test cases and slow exploration of deep program states. In this work, I present a hybrid fuzzing framework that integrates static and dynamic analysis with Large Language Model (LLM)-guided input mutation and semantic feedback. Static analysis extracts control-flow and data-flow information, which is transformed into structured prompts for the LLM to generate syntactically valid and semantically diverse inputs. During execution, I augment traditional coverage-based feedback with semantic feedback signals-derived from program state changes, exception types, and output semantics-allowing the fuzzer to prioritize inputs that trigger novel program behaviors beyond mere code coverage. I implement our approach atop AFL++, combining program instrumentation with embedding-based semantic similarity metrics to guide seed selection. Evaluation on real-world open-source targets, including libpng, tcpdump, and sqlite, demonstrates that our method achieves faster time-to-first-bug, higher semantic diversity, and a competitive number of unique bugs compared to state-of-the-art fuzzers. This work highlights the potential of combining LLM reasoning with semantic-aware feedback to accelerate and deepen vulnerability discovery.

Large Language Models (LLMs) enhanced with retrieval -- commonly referred to as Retrieval-Augmented Generation (RAG) -- have demonstrated strong performance in knowledge-intensive tasks. However, RAG pipelines often fail when retrieved evidence is incomplete, leaving gaps in the reasoning process. In such cases, abductive inference -- the process of generating plausible missing premises to explain observations -- offers a principled approach to bridge these gaps. In this paper, we propose a framework that integrates abductive inference into retrieval-augmented LLMs. Our method detects insufficient evidence, generates candidate missing premises, and validates them through consistency and plausibility checks. Experimental results on abductive reasoning and multi-hop QA benchmarks show that our approach improves both answer accuracy and reasoning faithfulness. This work highlights abductive inference as a promising direction for enhancing the robustness and explainability of RAG systems.

Rapid, large-scale 3D reconstruction from multi-date satellite imagery is vital for environmental monitoring, urban planning, and disaster response, yet remains difficult due to illumination changes, sensor heterogeneity, and the cost of per-scene optimization. We introduce SwiftGS, a meta-learned system that reconstructs 3D surfaces in a single forward pass by predicting geometry-radiation-decoupled Gaussian primitives together with a lightweight SDF, replacing expensive per-scene fitting with episodic training that captures transferable priors. The model couples a differentiable physics graph for projection, illumination, and sensor response with spatial gating that blends sparse Gaussian detail and global SDF structure, and incorporates semantic-geometric fusion, conditional lightweight task heads, and multi-view supervision from a frozen geometric teacher under an uncertainty-aware multi-task loss. At inference, SwiftGS operates zero-shot with optional compact calibration and achieves accurate DSM reconstruction and view-consistent rendering at significantly reduced computational cost, with ablations highlighting the benefits of the hybrid representation, physics-aware rendering, and episodic meta-training.