Browse Articles

Programmable DNA hydrogels for dual-mode PD-L1 suppression via polyvalent LYTAC mimics and transcriptional silencing

Immune checkpoint blockade has revolutionized oncology, yet low response rates and acquired resistance—often driven by inadequate Programmed death-ligand 1 (PD-L1) suppression—remain significant barriers. While degradation-based proteolysis-targeting chimeras offer a promising alternative to traditional antibodies, targeting the intracellular and transcriptional drivers of checkpoint expression remains a challenge. We report a programmable, tumor-responsive DNA hydrogel platform, synthesized via rolling circle amplification, designed for the comprehensive, dual-mode modulation of PD-L1. This modular nucleic acid framework codelivers polyvalent aptamer-based lysosome-targeting chimeras (LYTAC mimics) to induce extracellular PD-L1 degradation and siSMARCAL1 to silence the chromatin-remodeling-driven transcriptional activation of PD-L1. By integrating localized, sequential release within the tumor microenvironment, this system achieves a synergistic “degrade-and-silence” effect that effectively dismantles PD-1/PD-L1-mediated immunosuppression while concurrently triggering immunogenic cell death. In murine melanoma models, the hydrogel significantly suppressed primary tumor growth and prevented postoperative recurrence, eliciting a robust and durable systemic antitumor immune response. Our findings establish a versatile, DNA-based materials strategy for programmable protein degradation and multilevel checkpoint modulation, offering a generalizable approach for enhancing the efficacy of cancer immunotherapy.

Development of novel reinforcement learning-based optimizer to impede tumor growth via radiochemotherapy

Abstract Current cancer treatment strategies prioritize algorithmic robustness and efficiency but frequently neglect critical aspects of patient safety and comfort. These approaches typically rely on chemotherapy-based mathematical models optimized solely for short-term tumor reduction, disregarding the broader impact on patient health. This study introduces a patient-centered approach to optimize cancer treatment, balancing treatment efficacy and toxicity. The proposed research method incorporates both radiation therapy and chemotherapy simultaneously in the form of ordinary differential equations (ODE)-based mathematical dynamics. These updated dynamics are then utilized to propose a novel control mechanism that integrates nonlinear sliding mode control (SMC) with reinforcement learning-based proximal policy optimization (PPO) algorithm. Conventional sliding mode control (SMC) algorithm is first modified by replacing its signum function-based switching control with a sigmoid function to address issues like chattering and transients in treatment control. This smooth SMC is then incorporated within the framework of PPO to dynamically adjust treatment schedules, reduce drug and radiation dosages, smooth administration of treatment dosages, and enhance patient health indicators. Results showed that the proposed hybrid PPO method effectively lowered chemotherapy and radiotherapy dosages while maintaining tumor suppression, minimizing treatment toxicity, and improving immune cell recovery. In quantitative comparisons, the proposed PPO algorithm reduced baseline dosages by up to 76.8% for chemotherapy and 66% for radiotherapy and achieved tumor suppression 5.67% faster than conventional multi-input optimization methods. It also lowered cumulative treatment intensity by over 92%, demonstrating a substantial enhancement in patient safety. The methodological originality of this study lies in integrating nonlinear smooth SMC with reinforcement learning-based PPO within a patient-centered ODE modeling framework that jointly represents radiotherapy, chemotherapy, tumor dynamics, immune cell dynamics, healthy cell preservation, and health indicator state. The proposed framework provides a relevant, toxicity-aware computational approach for radio-chemotherapy dosage optimization, demonstrating lower simulated treatment intensity while maintaining tumor suppression under stated model assumptions. As a pre-clinical computational proof of concept, this work establishes a robust and interpretable basis for future treatment-planning studies, subject to retrospective clinical validation, patient-specific parameterization, and prospective safety evaluation.

Amazon deforestation reduces precipitation and soybean yields across Southern Brazil

Rapid agricultural expansion has driven forest loss worldwide. Deforestation reduces evaporation, potentially decreasing precipitation and crop yields far beyond agricultural frontiers. Here, we quantified the teleconnection impacts of Amazon deforestation on precipitation and soybean yields across Brazil during 1982–2018, using a Lagrangian moisture tracking model. Our analysis indicates that tree evaporation contributes one-third of growing-season precipitation, yet recent deforestation decreased seasonal precipitation by 6 to 30% across Brazilian soybean states, with replacement land covers providing limited compensation for the losses. Although precipitation declines were most pronounced near Amazonian deforestation, the largest yield reductions (227 kton, or ∼ 6% loss) occurred in the southern state of Rio Grande do Sul. Cumulatively, deforestation-driven precipitation declines resulted in a total soybean production loss of ∼ 700 kton. These findings reveal that expanding agriculture into forests undermines yields in established croplands, potentially creating a feedback where yield losses drive demand for additional forest clearing. Under continued deforestation and climate change, this feedback is likely to intensify, threatening Brazilian rainfed agriculture into the future.

Density-aware and energy-gradient based relay selection for enhanced routing in mobile sink wireless sensor networks

Shaping chaos in bilayer graphene cavities

Bilayer graphene cavities where electrons are confined within finite graphene flakes provide an alluring platform not only for the future nanoelectronic devices owing to the tunable energy gap but also for investigating the quantum nature of chaos due to the trigonal warping of their Fermi surface. Here we demonstrate that rotating the cavity boundary relative to the underlying lattice structure drives a quantum transition from nearly integrable dynamics to chaotic regime, observed as a concomitant crossover of eigenvalue statistics and eigenstate profiles. Complementing the full quantum treatment, we examine the classical backbone of this onset of chaos by employing semiclassical ray dynamics. Our results position bilayer graphene cavities as a promising venue for investigating and engineering quantum-chaotic behavior in graphene-based devices.

The effect of astaxanthin supplementation on inflammatory markers, lipid profile, and anthropometric indices in patients with heart failure: a randomized controlled trial

Dissecting hydrogen bond energetics to answer the age-old question: “How much do hydrogen bonds contribute to enzymatic catalysis?”

Enzymes are fundamental to life, providing the rapid reactions and specificity needed to sustain biological processes. While we have overcome the first major challenge in understanding enzymes—identifying their reaction mechanisms—the second major challenge of quantifying the contributions of each catalytic interaction and molecular mechanism remains largely unmet. In particular, hydrogen bonds are ubiquitous in enzyme active sites, yet their quantitative contributions to enzyme catalysis have remained a fundamental unresolved question. We first describe the limitations that prevent the assignment of hydrogen bond catalytic contributions from traditional approaches. These limitations are overcome by using linear free energy relationships (LFERs) to evaluate active site hydrogen bond energetics in a particularly amenable enzyme, ketosteroid isomerase (KSI). Multiple LFERs provide a consistent picture, suggesting that KSI’s active site hydrogen bond donors, tyrosine and protonated aspartic acid side chains, contribute to catalysis because they are inherently stronger hydrogen bond donors than water molecules, the donors in the analogous nonenzymatic reaction. These LFERs also provide evidence against models that invoke distinct enzyme environments that enhance hydrogen bond energetics, relative to aqueous solution. Instead, the LFERs suggest multiple dipoles in the protein and solvent environment surrounding the active site accommodate transition state charge accumulation and provide hydrogen bond energetics similar to what is observed in protic solvents. The quantitative models from this and prior studies allow us to quantitatively estimate the catalytic contributions changes from hydrogen bonds. This model, applied to additional enzymes, will test its generality and help identify additional mechanisms that enzymes may use to enhance catalysis.

Physics-guided cross-domain adaptation: a hierarchical hybrid transformer framework with contrastive learning for robust fault diagnosis under variable working conditions

Bioengineered zinc oxide nanoparticles derived from Teucrium polium as a multifunctional platform for anticancer activity, hemocompatibility, larval toxicity and photocatalytic remediation

Real-time identification of malignant breast tissue during electrosurgical resection

Abstract Achieving complete resection of breast cancer with clear margins remains a significant surgical challenge, particularly for infiltrating subtypes, where re-resection rates of up to 45% have been reported. Consequently, a device capable of providing real-time feedback to surgeons regarding the resected breast tissue holds the potential to significantly improve R0 resection rates. This study represents a further advancement toward intraoperative, real-time classification of breast tissue using optical emission spectroscopy (OES). The objective was to establish the feasibility of OES for distinguishing between normal and pathological breast tissue during electrosurgical incision. Spectra obtained from specimens of 80 patients were analyzed, including 68 patients who underwent breast cancer surgery and 12 patients who underwent risk-reducing or breast reduction surgery. Spectroscopic classification was performed of spectra from tumors with no special type (NST) invasive-lobular carcinoma (ILC) using a machine learning approach based on selected spectral features. The true positive rate reached 91,0% for NST spectra and 78.7% for ILC spectra with true negative rates of 95,2% for NST and 85,4% for ILC. In summary, the current support vector machine (SVM) algorithm demonstrates reliable classification performance for the predominant NST subtype. However, the accuracy of the ILC classification still needs to be improved. Further refinement of the OES-based classification approach is necessary to enhance its reliability across all breast cancer subtypes, particularly the rarer forms, thereby facilitating robust real-time detection during surgery.

Effect of organic material addition combined with tillage systems on organic carbon components and microbial communities in corn-fields of North East China

Biomechanical analysis of single leg deadlift under the effect of instability

Microfluidics-based engineered silver nanoparticles to control growth and biofilm formation in bacterial pathogens causing dental infection

Benchmarking machine learning approaches for polarization mapping in ferroelectrics using 4D-STEM

Abstract Four-dimensional scanning transmission electron microscopy (4D-STEM) provides rich, atomic-scale insights into materials structures. However, extracting specific physical properties—such as polarization directions essential for understanding functional properties of ferroelectrics—remains a significant challenge. In this study, we systematically benchmark multiple machine learning models, namely ResNet, VGG, a custom convolutional neural network, and PCA-informed k-Nearest Neighbors, to automate the detection of polarization directions from 4D-STEM diffraction patterns in ferroelectric potassium sodium niobate. While models trained on synthetic data achieve high accuracy on idealized synthetic diffraction patterns of equivalent thickness, the domain gap between simulation and experiment remains a critical barrier to real-world deployment. In this context, a custom-made prototype representation training regime and PCA-based methods, combined with data augmentation and filtering, represent promising steps toward bridging this gap. Nevertheless, their reliability for real-world applications remains to be explored in future work. Error analysis reveals periodic missclassification patterns, indicating that not all diffraction patterns carry enough information for a successful classification. Additionally, our qualitative analysis demonstrates that irregularities in the model’s prediction patterns correlate with defects in the crystal structure, suggesting that supervised models could be used for detecting structural defects. These findings guide the development of robust, transferable machine learning tools for electron microscopy analysis.

Optimization of Litsea cubeba ethanol extract and its antibacterial mechanism against Staphylococcus aureus

Experimental and 3D finite element modeling of line heating effect on thin mild steel sheets

Photodynamic therapy enhances dacarbazine sensitivity of melanoma cells by induction of apoptosis and downregulation of PI3K/AKT signaling pathway

Development of a psychological risk indicator and its association with sports injuries in football players

Mechanical properties of concrete reinforced with Elaeis Guineensis midrib fibres

Polyphasic taxonomic characterization of Brachybacterium netajii sp. nov., a metabolically versatile bacterium isolated from the river Ganges, India

Abstract A comprehensive polyphasic taxonomic strategy was applied to the systematic characterization of strain DNPG3 T , which was isolated from the river Ganges, Hooghly, West Bengal, India. The Gram-positive, halotolerant, heavy-metal-tolerant strain exhibited the ability to degrade p -nitrophenol (PNP). Cellular fatty acid analysis revealed that the predominant components were anteiso-C 15:0 (24.61%), C 11:0 (21.06%), iso-C 16:0 (11.89%), C 16:0 (11.58%), and anteiso-C 17:0 (11.24%). Notably, the presence of C 11:0 , C 10:0 2-OH as major fatty acids differentiate strain DNPG3 T from its closely related members of the genus Brachybacterium . The predominant respiratory quinone was identified as menaquinone-7 (MK-7). Analysis of 16S rRNA gene sequence indicated that B. zhongshanense strain JB T was the closest relative of DNPG3 T , sharing 97.08% sequence similarity. Genome-based ANI value calculated using the EzBioCloud server revealed that B. zhongshanense JCM 15471 T was the closest genomic relative (85.49%). These values were further substantiated by digital DNA–DNA hybridization (dDDH) estimates calculated using the GGDC server. Taxonomic assignment using the GTDB database further indicated that strain DNPG3 T constitutes a previously unrecognized species within the genus Brachybacterium . Genome analysis of strain DNPG3 T identified eleven genomic islands, along with a rich repertoire of 194 carbohydrate-active enzyme (CAZyme) families, comprising 95 glycoside hydrolases and 53 glycosyltransferases. In addition, five biosynthetic gene clusters were detected. Collectively, these genomic features indicate the involvement of horizontal gene transfer events and highlighted the pronounced metabolic versatility of the strain, underscoring its potential for industrial enzyme production and secondary metabolite biosynthesis. Pan-genome analysis further indicates that the Brachybacterium pan-genome is open, reflecting substantial genetic diversity and ongoing gene acquisition within the genus. Comprehensive biochemical, physiological, chemotaxonomic, and phylogenetic analyses supported the assignment of strain DNPG3 T to the genus Brachybacterium while clearly distinguishing it from all currently described species within the genus. Accordingly, strain DNPG3 T was proposed to represent a novel species, for which the name Brachybacterium netajii sp. nov. is suggested. The type strain was DNPG3 T (= MTCC13125 T ).