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Cancer care is increasingly driven by molecular classification, yet many key oncogenic drivers remain undruggable, and intrinsic or acquired resistance to treatment frequently limits durable clinical benefit. CRISPR-Cas technologies provide a modular, programmable platform to interrogate and directly manipulate cancer biology via sequence-specific targeting of DNA or RNA and have advanced from experimental tools to the early stages of clinical translation. In this Review, we outline how CRISPR-enabled functional genomics approaches can reveal unexpected cancer dependencies and resistance mechanisms. We discuss emerging applications of CRISPR-based diagnostics in oncology that convert precise nucleic acid sequence recognition into rapid mutation detection. We also discuss applications of CRISPR in therapeutic strategies ranging from ex vivo immune cell engineering to nascent in vivo interventions that directly target tumour-related sequences such as fusion junctions or single-nucleotide variants. Finally, we highlight technological and regulatory challenges, including effective delivery of the editing machinery to cells in vivo, safety and platform-level regulatory frameworks, that will determine the clinical utility of CRISPR-based diagnostics and therapies in oncology.

Postoperative recurrence severely limits long-term survival after radical resection for pancreatic ductal adenocarcinoma (PDAC), yet the spatiotemporal heterogeneity and determinants of recurrence remain incompletely understood. We conducted a single-center retrospective study of PDAC patients who underwent radical surgery at our institution between 2014 and 2024. Regression analyses were used to identify factors associated with temporal (early [≤ 1 year] vs. late [> 1 year] recurrence) and spatial (site of initial recurrence) heterogeneity. Among 1,065 enrolled patients, 879 experienced recurrences, with cumulative recurrence rates of 55.7, 76.6, and 92.3% at 1, 2, and 5 years, respectively. Early recurrence occurred in 593 patients and late recurrence in 286. Initial recurrence patterns included local (22.4%), liver (52.3%), lung (5.8%), peritoneal (12.7%), and mixed/other metastases (6.7%). Male, poor tumor differentiation, advanced pathological stage, and lack of adjuvant therapy were independent factors for early recurrence. Liver and peritoneal metastases were independently associated with early recurrence and shorter recurrence-free survival (RFS), whereas lung metastasis correlated with later recurrence and longer RFS. Key factors influencing RFS varied by recurrence site: for local recurrence-poor differentiation, nodal metastasis, and no adjuvant therapy; for liver metastasis-larger tumor size, poor differentiation, nodal metastasis, and no adjuvant therapy; for lung metastasis-larger tumor size and nodal metastasis; for peritoneal metastasis-larger tumor size, surgical procedure, nodal metastasis, and no adjuvant therapy. Postoperative recurrence in PDAC demonstrates significant spatiotemporal heterogeneity, with timing-specific and site-specific risk factors. These findings support tailored follow-up strategies and refined risk assessment for recurrent PDAC.

The rapid rise in global population and industrial activity has intensified environmental challenges, particularly carbon dioxide (CO₂) emissions from the cement and concrete industry. Biochar, a carbon-rich byproduct of biomass pyrolysis, has emerged as a promising solution for sustainable construction by enhancing carbon sequestration and improving mechanical performance when partially substituting cement. This study integrates experimental evidence with advanced machine learning (ML) techniques to evaluate the compressive strength, cost-efficiency, and carbon footprint of biochar-incorporated concrete. A comprehensive dataset of nine input parameters, including cement, aggregates, silica fume, fly ash, biochar, water, superplasticizer, and curing age was modeled using multiple ML approaches. Among the models tested, the hybrid XGB-Histogram Gradient Boosting (XGB-HistGB) model consistently achieved the best overall performance, with a testing R2 of 0.958, the lowest mean absolute error (3.03), and minimal prediction bias. This model outperformed standalone algorithms and other hybrids, providing reliable accuracy across compressive strength, cost, and embodied CO₂ predictions. SHAP and partial dependence analyses confirmed fine aggregate, curing age, and superplasticizer as the most influential parameters, while biochar dosage required careful optimization to balance strength retention with sustainability benefits. A user-friendly graphical interface was also developed, enabling real-time prediction of compressive strength, material cost, and CO₂ emissions based on user-defined mix proportions. Overall, the findings demonstrate that biochar can be effectively integrated into sustainable concrete formulations, and the XGB-HistGB model offers a powerful AI-driven predictive framework to optimize both structural performance and environmental outcomes.

Understanding the predictive associations among anthropogenic [Formula: see text] emissions, economic development, and land-use change is important for sustainability-oriented analysis in vulnerable coastal regions. This study examines these environmental-economic linkages in four member countries of the Indian Ocean Rim Association (IORA), namely Malaysia, Mauritius, Sri Lanka, and Madagascar, using historical data from 1960 to 2020. To this end, a machine learning-based forecasting framework, termed Xavier Online Sequential Extreme Learning Machine with Genetic Algorithm (XOS-ELM-GA), is proposed to predict GDP and agricultural land area using [Formula: see text] emissions as a key explanatory indicator. Given the nonlinear and dynamic nature of the statistical relationships among these variables, advanced forecasting methods are required. Preliminary statistical analyses were first conducted to examine the historical relationships among [Formula: see text] emissions, GDP, and agricultural land area in the selected countries. On this basis, the proposed XOS-ELM-GA framework was developed to perform correlation-based forecasting rather than causal inference. Methodologically, the model integrates Xavier-based weight initialization and an optimized genetic algorithm to improve forecasting accuracy while mitigating common limitations of conventional OS-ELM, including prediction uncertainty, parameter sensitivity, and computational inefficiency. Experimental results show that XOS-ELM-GA consistently outperformed ELM, OS-ELM, and OS-ELM-GA across the four IORA countries. For example, in annual GDP forecasting, it achieved average MSE/SMAPE values of 4.47E-03/10.13% for Malaysia, 1.12E-03/10.48% for Mauritius, and 2.98E-02/12.13% for Sri Lanka. In agricultural land forecasting, the model also delivered competitive performance, with average SMAPE values of 3.77% for Sri Lanka and 2.99% for Madagascar. These findings demonstrate that the proposed model provides more reliable forecasts than standard ELM-based online variants. The resulting forecasts offer useful decision support for policymakers seeking to balance economic development with environmental sustainability in IORA countries, and provide practical insights for advancing the United Nations Sustainable Development Goals, particularly SDG 8, SDG 13, and SDG 15.

Despite advances in systemic therapies for patients with (locally) advanced stage lung cancer, response rates remain poor, underscoring the urgent need for predictive models to guide therapy selection. Patient-derived ex vivo models are promising, however, their clinical utility is restricted by the need for surgical biopsies, low establishment rates, and culture durations exceeding the clinically-relevant therapeutic decision-making window. Here, we developed and clinically validated a rapid, high-throughput ex vivo 3D tumor replica platform that enables functional drug testing from small diagnostic biopsies. In this prospective multicenter cohort study of 129 treatment-naïve lung cancer patients, tumor replicas were successfully established in 65% of biopsies. These cultures retained their original morphological, genetic, and immunophenotypic features. Ex vivo drug responses to chemotherapy and targeted agents were generated within a median of 12 days from biopsy acquisition. The ex vivo drug responses were in concordance with the treatment responses in patient-derived xenografts and lung cancer patients. In the clinical study, the ex vivo platform demonstrated a sensitivity of 73% and a positive predictive value of 92%. External validation confirmed the feasibility and reproducibility of the platform. In conclusion, this platform enables rapid patient-specific drug response assessment from routine biopsies within a clinically relevant time-frame.

Prostate cancer (PC) is the fourth most common cancer worldwide and is recognised as one of the major men's health challenges of the twenty-first century. There has been a steady increase in PC incidence, particularly for advanced-stage PC cases, which have increased by 5% since 2014. Despite advances in diagnostic and treatment techniques, understanding of the genetic mechanisms underlying PC remains limited, especially in families with multiple cases of the disease, where genetic predispositions significantly increase the risk of developing cancer. This chapter provides a comprehensive and up-to-date review of PC, covering key aspects such as its epidemiology, aetiology and associated risk factors. From the hereditary point of view, this chapter covers the main genes associated with the susceptibility to develop PC and related pathologies. Additionally, the chapter evaluates the current paradigm of detection and treatment protocols, while also exploring future research directions.

Abiotic stress tolerance has been significantly weakened in modern crops during the domestication process. Regaining tolerance has become a critical task in light of current climate trends and their impact on global food security. Abiotic stress tolerance is an extremely complex trait and is conferred at various levels of plant functional organization and developmental stages, with regulatory mechanisms operating across multiple scales, from individual cells to tissues and the entire plant. The emergence of advanced molecular tools such as single-cell RNA sequencing and spatial omics technologies has revolutionized the field, advancing our understanding of plant responses to hostile environments. However, the implementation of this knowledge in crop breeding programmes is handicapped by the lack of appropriate phenotyping platforms. Here, we argue that current phenotyping methods may be excellent tools for functional validation of previously discovered traits but have limited predictive value in stress biology. We also propose that bridging the mismatch between omics technologies and phenotyping is the only way to account for cell-specific operation of key genes conferring stress tolerance and implementing them in breeding programmes. Some practical examples using cell-based phenotyping tools such as fluorescence dyes or electrophysiological methods are given, and current limitations and prospects of cell-based phenotyping are discussed.

High-power lasers offer ultrahigh intensities for plasma interactions, but they lack advanced techniques to control the properties of the fields, because no optical elements could withstand their high intensities. The vibrant field of metasurfaces has transformed modern optics by enabling unprecedented control over light at subwavelength through deliberate design. However, metasurfaces have traditionally been limited to solid-state materials and low light intensities. Extending the sophisticated capabilities of metasurfaces from solids into the plasma realm would open new horizons for high-field science. Here, we present a proof-of-concept experimental demonstration of plasma-state metasurfaces (PSMs) via the photonic spin Hall effect and the generation of stable-propagating vortex beams under intense laser irradiation. Time-resolved pump-probe measurements reveal that the functionality of PSMs can persist for several picoseconds, making them suitable for controlling ultra-intense femtosecond lasers, even in state-of-the-art multi-petawatt systems. Harnessing the powerful toolkit of metasurfaces, this approach holds the promise to revolutionize our ability to manipulate the amplitude, phase, polarization, and wavefront of high-power lasers during their pulse duration. It also opens new possibilities for innovative applications in laser-plasma interactions such as compact particle acceleration and novel radiation sources.

Esophageal cancer is an aggressive malignancy with high morbidity, mortality, and limited durable treatment options due to tumor heterogeneity, immune evasion, and recurrence. This study addresses these challenges by computationally designing a novel CTL-based multi-epitope vaccine using experimentally validated epitopes from cancer-testis antigens (NY-ESO-1 and MAGE-A family), which are overexpressed in esophageal squamous cell carcinoma. To the best of our knowledge, this represents one of the most comprehensive in silico investigations for esophageal cancer, uniquely integrating experimentally validated epitopes with advanced immunoinformatics, high-resolution structural modeling, molecular dynamics, and immune simulation strategies. Nine experimentally validated CTL epitopes were retrieved from IEDB and rigorously evaluated for antigenicity (VaxiJen), toxicity (ToxinPred), allergenicity (AllerTOP), and IFN-γ induction (IFNepitope). A 253-amino-acid multi-epitope construct was assembled with AAY/EAAAK linkers, PADRE adjuvant, and 5 S rRNA-derived TLR4 agonist. Physicochemical properties were assessed (ProtParam, SOLpro); secondary/tertiary structures predicted (SOPMA, trRosetta); and validated (ProSA, Ramachandran). B-cell epitopes were predicted with ElliPro. Molecular docking (ClusPro) with TLR4, 100-ns MD simulations (GROMACS), and MM/GBSA binding free energy calculations were performed. Immune responses were simulated using C-ImmSim, and population coverage was analyzed via IEDB. The vaccine construct demonstrated excellent stability (instability index 31.16), solubility (0.577), and antigenicity (VaxiJen 0.5734; non-allergenic). It exhibited a predominantly α-helical structure (64.43%) with high model quality (ProSA Z-score: - 6.33). Strong TLR4 binding was confirmed (-910.7 kJ/mol, stable RMSD ~ 0.29 nm, MM/GBSA - 110.76 kcal/mol). Immune simulations predicted robust IgG/IgM responses, memory cell formation, and elevated IFN-γ (> 4 × 10⁵ ng/mL). Global population coverage reached 50.02%. This novel CTL-based multi-epitope vaccine candidate is stable, immunogenic, and capable of eliciting strong anti-tumor immunity. It provides a promising computational platform for esophageal cancer immunotherapy, warranting experimental validation and clinical translation.

Evaluating large language models (LLMs) in the mental health domain presents distinct challenges due to the subtle, context-dependent, and subjective nature of psychological symptoms. We introduce PsyEval, a benchmark specifically designed to evaluate LLMs in mental health-related tasks across three core dimensions: knowledge, diagnosis, and emotional support. PsyEval is constructed to reflect the complexity of mental health scenarios and provides a structured framework for assessing model performance within this sensitive domain. Using PsyEval, we evaluate eleven advanced LLMs with different prompting strategies to investigate how prompting affects their responses. The results reveal considerable gaps in LLMs' current ability to reason accurately and respond appropriately in mental health contexts, while also indicating promising directions for future model enhancement.

Circulating tumor cells (CTCs) display a range of specific mechanical properties that set them apart from tumor-residing cells. CTCs face a series of mechanobiology-related challenges: they need to break from the tumor, intravasate, withstand fluid shear stress (FSS) in circulation and then attach to the vessel wall for extravasation. Established carcinoma cell lines are adherent and grow on the surface of cell culture dishes. Such cells may represent tumor residents or advanced colonization of the metastatic niche; however, they are not adequate models of CTCs. Here, we describe the generation of "model CTCs" by subjecting carcinoma cell lines to circulation-imitating FSS in a microfluidic system. Such cells can be characterized by analytical methods of choice, including atomic force microscopy (AFM) based nanomechanical phenotyping. Indeed, model CTCs display nanomechanical properties resembling patient-isolated CTCs and are well suited for studies on mechanotransduction.

Recent emphasis on transdisciplinary teams in science offers an opportunity for biostatisticians to assume roles beyond data management and analysis. Biostatisticians contribute to all phases of research, from hypothesis formulation and rigorous study design to data analysis and interpretation. Yet, their contributions are often underrecognized. We argue for better integration of biostatisticians in research training and team structures. Postgraduate physician-scientist training should include mentorship from biostatisticians and education on collaborative skills. Fully integrating biostatisticians as co-investigators improves rigor, reproducibility, and innovation. Institutional support, including protected time and updated promotion criteria, is essential to support sustained collaboration. Structural changes must address limited access to biostatistical resources, especially in institutions lacking Clinical and Translational Science Awards. Clarifying roles and responsibilities at the outset, including through tools like team charters, facilitates alignment and prevents conflict. Elevating biostatisticians as scientific partners strengthens team dynamics and improves research quality. Team-based biomedical research-when genuinely collaborative-synergistically advances both methodological rigor and health outcomes. This perspective is especially relevant for general internists engaged in increasingly complex, transdisciplinary research. The future of health science innovation will depend on dismantling traditional silos and promoting meaningful, equitable scientific partnerships across disciplines.

The choroid plexus (CP) plays a crucial role in cerebrospinal fluid secretion and the maintenance of brain homeostasis. Its structure and function have been implicated in the pathogenesis of dementia; however, the longitudinal associations of CP with dementia and structural brain biomarkers remain unclear. This prospective cohort study utilized data from the UK Biobank, including 45,306 participants (mean age, 64 years; 47.2% men) who underwent 3.0&#x2009;T multiparametric brain MRI scans. CP volume and signal intensity were quantified by FreeSurfer software. Measures of grey or white matter macrostructures or microstructures were derived from structural or diffusion MRI. Dementia outcomes were identified using linkage of hospital admission records or death register. Tests for global and domain-specific cognitive functions were administrated at baseline and follow up. Data were analyzed using Cox proportional hazards, Mendelian randomization, linear mixed-effects models, and mediation models. Advancing age was correlated with increased CP volume (R&#x2009;=&#x2009;0.49, P&#x2009;<&#x2009;0.001) and decreased CP signal intensity (R&#x2009;=&#x2009;-0.44, P&#x2009;<&#x2009;0.001). Meanwhile, greater CP volume was associated with an increased risk of all-cause dementia (hazard ratio, 1.61; 95% confidence interval, 1.32-1.97), while higher CP intensity was correlated with a reduced dementia risk (0.44; 0.36-0.55). Reduced volumes of the hippocampus, amygdala, and nucleus accumbens, increased white matter hyperintensity volume, mean diffusivity, and isotropic compartment volume fraction, and decreased intracellular volume fraction significantly mediated up to 42.9% of these associations. This study provides evidence supporting a causal relationship between CP morphological parameters and dementia risk that is partly mediated by specific brain phenotypes, and further suggests that the CP parameters may be valuable biomarkers for structural brain aging and dementia.

The recognition of human actions through visual input poses considerable difficulties owing to the diverse ways in which individuals perform the same actions, the temporal variations that are intrinsic to these actions, and the differing viewpoints from which they are perceived. In order to address the limitations of single-modality methods, researchers have increasingly adopted multimodal visual data fusion strategies. This study presents an optimized deep learning model designed for action recognition by combining multiple data modalities, such as depth, skeleton, and inertial data through applying multi-agent systems. The study utilized a frame selection method based on the displacement of skeleton joints with uniform resampling for selection over the temporal-space; further, the study employed the HOG extractor on depth selected frames, 1D convolutional block for skeleton and inertial features extracting, and LSTM to process temporal patterns. Furthermore, an adaptive cross-attention method is developed to dynamically assess and implement the relative significance of the learned long-range temporal dependencies from each input modality. The effectiveness of the model is evaluated using the publicly available UTD MHAD dataset, demonstrating enhanced performance compared to existing leading action recognition techniques, achieving accuracy of 96.99%. A thorough assessment methodology, which includes confusion matrices, ROC curves, t-SNE visualizations, and average attention heatmaps, is utilized to confirm the model's robustness and to provide insights on its performance. The results underscore the model's capability in utilizing multi-modal data fusion and sophisticated temporal feature extraction, presenting a promising strategy for advancing action recognition tasks.

Wetlands are highly productive ecosystems that regulate hydrological processes, store carbon, and support rich biodiversity, yet they remain highly vulnerable to hydro-climatic variability and anthropogenic pressures. This study examines long-term eco-hydrological dynamics influencing wetland systems in the Madhubani district of North Bihar during 2002-2024 using an integrated framework of climatic, hydrological, and ecological indicators. Rainfall trends were analyzed alongside Actual and Potential Evapotranspiration (ET, PET), Standardised Water Deficit (SWD), Wetness Index (WI), Gross and Net Primary Productivity (GPP, NPP), and remote sensing indices, including the Modified Normalized Difference Water Index (MNDWI) and Soil-Adjusted Vegetation Index (SAVI) derived from Terra MODIS and Landsat datasets. Trend analysis indicates a non-significant but persistent decline in annual rainfall (Z&#x2009;=&#x2009;-&#xa0;1.33; Sen's slope&#x2009;=&#x2009;-&#xa0;7.53&#xa0;mm year-1), with stronger decreases during the monsoon season (-&#xa0;8.65&#xa0;mm year-1). ET (320-720&#xa0;mm yr-1) remained substantially lower than PET (2030-2520&#xa0;mm yr-1), indicating persistent atmospheric water demand and increasing moisture stress, as reflected in Standardised Water Deficit variability and relatively low Wetness Index values. Vegetation productivity exhibited considerable interannual variability, with GPP ranging from&#x2009;~&#x2009;770 to 1530&#xa0;g C m-2&#xa0;yr-1 and NPP from&#x2009;~&#x2009;100 to 430&#xa0;g C m-2&#xa0;yr-1. Both productivity metrics declined sharply during 2012-2015, suggesting sensitivity to monsoon rainfall variability. Spatial analysis reveals significant hydrological and ecological changes. Post-monsoon moderately wet areas declined from 75.10% (2002) to 55.27% (2024), while dry zones expanded from 17.24 to 39.55%, indicating progressive wetland contraction. Vegetation dynamics derived from SAVI show spatial redistribution of vegetation cover, with dense vegetation increasing from 21.32 to 26.18%, particularly in northern areas associated with irrigation supported agriculture. Correlation analysis further demonstrates strong eco-hydrological coupling, with rainfall positively correlated with the wetness index and negatively associated with water deficit. Overall, the findings indicate a shifting eco-hydrological regime in Madhubani characterized by declining rainfall reliability, increasing moisture stress, wetland shrinkage, and changing vegetation patterns. These trends highlight the need for integrated wetland management and climate-resilient water resource strategies to sustain ecological stability in the floodplain landscapes of North Bihar.

Cardiogenic shock (CS) remains highly morbid despite significant advancements in management. A cornerstone of this management is Venoarterial Extracorporeal Membrane Oxygenation (VA ECMO). However, consensus surrounding the management of VA ECMO for CS, including the use of concomitant Left Ventricular Mechanical Unloading (LVMU), remains limited. We thus conducted a national survey of practice patterns in the treatment of CS to characterize such variability. Surveys were distributed to physicians involved in management of VA ECMO across a variety of center types. Responses representing 67 institutions were analyzed. VA ECMO was used for a median of 10% of CS patients at each institution. Formal shock teams were present at 64.1% of centers and were associated with both higher annual VA ECMO volume (P&#x27e8;0.01) and greater intra-aortic balloon pump use prior to VA ECMO (p&#x2009;=&#x2009;0.03). LVMU was employed by 63.4% of centers, most commonly using Impella (93.3%), with unloading initiated at ECMO cannulation in a median of 30% of cases. Triggers and targets for LVMU varied widely, though pulmonary capillary wedge pressure was the most common endpoint. These findings highlight substantial heterogeneity in CS diagnosis, VA ECMO initiation, and LVMU strategies, underscoring the need for prospective studies to define optimal care.

Advances in imaging- and sequencing-based spatial transcriptomics have increased molecular throughput and resolution, enabling the measurement and analysis of spatial transcriptomes at single-cell resolution. However, accurate cell segmentation remains challenging because cell morphology, tissue processing and staining methods vary across samples and platforms, limiting the accuracy and generalizability of existing algorithms. Here we show that DISSECT, a cell segmentation model integrating cytological images with spatial transcriptomic profiles, improves spatial single-cell transcriptome reconstruction. DISSECT uses a pretrained deep generative model to denoise multiscale image features, predicts cell instances with an instance-aware detection module and applies image- and transcriptome-derived gradient fields to refine segmentation masks. Benchmarking across multiple datasets showed that DISSECT achieved higher mean average precision than several existing segmentation tools. We further applied DISSECT to three pairs of gastric adenocarcinoma samples collected before and after anti-PD-1 treatment and profiled by Stereo-seq, illustrating its utility for downstream spatial biological interpretation.