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Artificial intelligence (AI) has been widely applied in drug discovery with a major task as molecular property prediction. Despite booming techniques in molecular representation learning, key elements underlying molecular property prediction remain largely unexplored, which impedes further advancements in this field. Herein, we conduct an extensive evaluation of representative models using various representations on the MoleculeNet datasets, a suite of opioids-related datasets and two additional activity datasets from the literature. To investigate the predictive power in low-data and high-data space, a series of descriptors datasets of varying sizes are also assembled to evaluate the models. In total, we have trained 62,820 models, including 50,220 models on fixed representations, 4200 models on SMILES sequences and 8400 models on molecular graphs. Based on extensive experimentation and rigorous comparison, we show that representation learning models exhibit limited performance in molecular property prediction in most datasets. Besides, multiple key elements underlying molecular property prediction can affect the evaluation results. Furthermore, we show that activity cliffs can significantly impact model prediction. Finally, we explore into potential causes why representation learning models can fail and show that dataset size is essential for representation learning models to excel. AI has become a crucial tool for drug discovery, but how to properly represent molecules for data-driven property prediction is still an open question. Here the authors evaluate 62,820 models to highlight existing challenges, the impact of activity cliffs, and the crucial role of dataset size.

Copper plays vital roles in numerous cellular processes and its imbalance can lead to oxidative stress and dysfunction. Recent research has unveiled a unique form of copper-induced cell death, termed cuproptosis, which differs from known cell death mechanisms. This process involves the interaction of copper with lipoylated tricarboxylic acid cycle enzymes, causing protein aggregation and cell death. Recently, a growing number of studies have explored the link between cuproptosis and cancer development. This review comprehensively examines the systemic and cellular metabolism of copper, including tumor-related signaling pathways influenced by copper. It delves into the discovery and mechanisms of cuproptosis and its connection to various cancers. Additionally, the review suggests potential cancer treatments using copper ionophores that induce cuproptosis, in combination with small molecule drugs, for precision therapy in specific cancer types.

Interdisciplinary research has become a pivotal strategy for addressing complex challenges, and understanding its relationship with disruptive innovation has garnered increasing attention. This study explores the relationship between interdisciplinarity—measured by disciplinary variety, balance, and disparity—and disruptive innovation, quantified using the disruptive index. Focusing on Earth Sciences from the Web of Science database between 2000 and 2019, we construct a logistic regression model to examine these variables. Our findings reveal that interdisciplinarity indicators generally positively influence disruptive innovation. Besides, the analysis highlights the moderating effects of collaboration patterns and collaboration size. Notably, international collaboration plays a vital role in strengthening this connection. Regarding collaboration size, medium-sized teams are more effective at balancing efficiency and diversity, thereby effectively leveraging the strengths of various disciplines to foster innovation. Additionally, large teams can maximize their innovative potential through effective management strategies that integrate diverse perspectives. The results are validated through robustness checks. These insights contribute to a deeper understanding of strategically leveraging collaboration to foster interdisciplinary advancement and facilitate transformative progress.

The discovery of lactylation, a post-translational modification derived from lactate, has fundamentally altered the perception of cancer metabolism. Once regarded as a metabolic waste product, lactate is now recognized as a central fuel source, a signaling molecule, and an epigenetic substrate capable of reprogramming gene expression and cellular function. Lactylation integrates metabolic reprogramming, tumor plasticity, and immune suppression, thereby orchestrating cancer initiation, progression, and resistance to therapy. This review provides a critical and integrative commentary on recent advances in lactylation biology, drawing from biochemical, epigenetic, and immunological perspectives. It synthesizes mechanistic insights into lactylation, highlights its role in tumorigenesis and the tumor microenvironment (TME), and evaluates therapeutic strategies that target lactate production, transport, and lactylation machinery. By dissecting consensus, controversies, and unresolved questions, we argue that lactylation represents both a hallmark of tumor adaptation and a potential Achilles’ heel for intervention. We further discuss future research directions, including comprehensive lactylome mapping, structural biology of lactylated proteins, microbiome-derived lactate, and clinical translation. Ultimately, lactylation is not merely a byproduct of glycolysis but a metabolic language that tumors employ to communicate, adapt, and thrive. Decoding this language may open new frontiers in cancer therapy.

The digital economy and ecological environment are two major issues related to high-quality economic development. Scholars have not yet reached a unified conclusion about the link between the digital economy and pollution emissions, and the impact mechanism of the former on the latter needs further study. Using data from 278 Chinese cities from 2010 to 2019, this research employs coupling coordination analysis, fixed effect analysis and mediation analysis to examine the heterogeneous impact mechanisms of the expansion of the digital economy on urban pollution reduction from many angles. It discovers that, first, the growth of the digital economy has decreased the discharge of urban pollutants overall. Second, the impact mechanisms of the digital economy are heterogeneous. From a regional perspective, industrial structure supererogation plays an intermediary role in the relationship between digital economy development and pollution reduction in the eastern and central regions, but the mediating effect is not significant in the western and northeastern regions. In terms of the city development level, industrial structure supererogation has significantly mediated the relationship between the growth of the digital economy and the reduction of pollution in first- and second-tier cities, but this mediating effect is not significant in third-tier and other cities. Third, the above conclusions are still valid after the robustness test is carried out using instrumental variable estimation, replacement of the estimation method, and replacement of explanatory variables. This study is a useful contribution to research on the effects of the digital economy and the factors influencing pollution reduction. The results advance the study of the digital economy and also have practical implications for improving China’s ecological environment and fostering high-quality economic growth. Finally, we provide policy suggestions for the coordinated promotion of the digital economy’s development, industrial structure supererogation and environmental pollution reduction.

The discovery of microbial communities residing within tumors has unveiled a new dimension of cancer biology. In lung cancer, the intratumoral microbiome—comprising bacteria, fungi, and viruses—has emerged as a critical modulator of tumorigenesis, immune evasion, therapeutic response, and metastasis. This review comprehensively examines the landscape of the lung tumor microbiota, highlighting its mechanistic roles in shaping the tumor microenvironment, altering host immune responses, and reprogramming of cancer metabolism. We discuss the influence of specific microbial taxa on immunotherapeutic efficacy, including their interplay with immune checkpoints and pro-inflammatory signaling pathways. Moreover, we evaluate current evidence linking microbial signatures for diagnostic and prognostic applications, emphasizing their potential in biomarker discovery and precision oncology. By integrating findings from molecular epidemiology, multi-omics profiling, and preclinical models, this review provides a translational framework for leveraging the tumor-resident microbiota as both a within tumors, we may develop new microbiome-based strategies. These strategies could improve treatment outcomes and help overcome resistance to immunotherapy.

Lung cancer continues to be the leading cause of cancer-related mortality worldwide, accounting for more deaths than breast, colorectal, and prostate cancers combined. Over the past decade, the introduction of immunotherapy has reshaped treatment paradigms, offering hope for long-term survival in a disease historically associated with dismal outcomes. The incorporation of immune checkpoint inhibitors (ICIs) into the treatment of non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) has improved outcomes across early-stage, locally advanced, and metastatic settings. However, only a fraction of patients derive durable benefit, and challenges remain in overcoming resistance, predicting response, managing toxicity, and ensuring equitable access. This review provides a comprehensive overview of current progress in lung cancer immunotherapy. It discusses the immunobiology of lung tumors, the role of checkpoint blockade across disease stages, mechanisms of resistance, biomarker development, and combination strategies. Emerging modalities, including bispecific antibodies, CAR- and TCR-based cellular therapies, natural killer (NK) cell platforms, cytokine agonists, oncolytic viruses, and vaccines, are explored in depth. We also evaluate the translational significance of preclinical models, toxicity management, and issues of equity and accessibility. Finally, we outline key future directions that may redefine lung cancer immunotherapy in the coming years. Collectively, these advances highlight a transition from broad applications of checkpoint inhibition toward stage-specific, biomarker-driven, and multimodal immunotherapy approaches designed to convert temporary responses into durable remissions and, ultimately, cures.

Traditional approaches for the assessment of physiological responses of microbes in the environment rely on bulk filtration techniques that obscure differences among populations as well as among individual cells. Here, were report on the development on a novel micro-scale sampling device, referred to as the \"Single-probe,\" which allows direct extraction of metabolites from living, individual phytoplankton cells for mass spectrometry (MS) analysis. The Single-probe is composed of dual-bore quartz tubing which is pulled using a laser pipette puller and fused to a silica capillary and a nano-ESI. For this study, we applied Single-probe MS technology to the marine dinoflagellate , assaying cells grown under different illumination levels and under nitrogen (N) limiting conditions as a proof of concept for the technology. In both experiments, significant differences in the cellular metabolome of individual cells could readily be identified, though the vast majority of detected metabolites could not be assigned to KEGG pathways. Using the same approach, significant changes in cellular lipid complements were observed, with individual lipids being both up- and down-regulated under light vs. dark conditions. Conversely, lipid content increased across the board under N limitation, consistent with an adjustment of Redfield stoichiometry to reflect higher C:N and C:P ratios. Overall, these data suggest that the Single-probe MS technique has the potential to allow for near metabolomic analysis of individual phytoplankton cells, opening the door to targeted analyses that minimize cell manipulation and sampling artifacts, while preserving metabolic variability at the cellular level.

Attention control is a basic behavioral process that has been studied for decades. The currently best models of attention control are deep networks trained on free-viewing behavior to predict bottom-up attention control – saliency. We introduce COCO-Search18, the first dataset of laboratory-quality goal-directed behavior large enough to train deep-network models. We collected eye-movement behavior from 10 people searching for each of 18 target-object categories in 6202 natural-scene images, yielding ∼ 300,000 search fixations. We thoroughly characterize COCO-Search18, and benchmark it using three machine-learning methods: a ResNet50 object detector, a ResNet50 trained on fixation-density maps, and an inverse-reinforcement-learning model trained on behavioral search scanpaths. Models were also trained/tested on images transformed to approximate a foveated retina, a fundamental biological constraint. These models, each having a different reliance on behavioral training, collectively comprise the new state-of-the-art in predicting goal-directed search fixations. Our expectation is that future work using COCO-Search18 will far surpass these initial efforts, finding applications in domains ranging from human-computer interactive systems that can anticipate a person’s intent and render assistance to the potentially early identification of attention-related clinical disorders (ADHD, PTSD, phobia) based on deviation from neurotypical fixation behavior.

Ambient mass spectrometry imaging (MSI) is an emerging field with great potential for the detailed spatial analysis of biological samples with minimal pretreatment. We have developed a miniaturized sampling and ionization device, the Single-probe, which uses in-situ surface micro-extraction to achieve high detection sensitivity and spatial resolution during MSI experiments. The Single-probe was coupled to a Thermo LTQ Orbitrap XL mass spectrometer and was able to create high spatial and high mass resolution MS images at 8 ± 2 and 8.5 μm on flat polycarbonate microscope slides and mouse kidney sections, respectively, which are among the highest resolutions available for ambient MSI techniques. Our proof-of-principle experiments indicate that the Single-probe MSI technique has the potential to obtain ambient MS images with very high spatial resolutions with minimal sample preparation, which opens the possibility for subcellular ambient tissue MSI to be performed in the future. Graphical Abstract ᅟ