Browse Articles

Fibroblast‐Mimetic Lignin Polymersomes for Logic‐Gated Synthesis of Mechanically Reconfigurable Bioskins

ABSTRACT Ageing is inevitable and accompanied by progressive loss of skin elasticity. Fibroblasts, embedded within the extracellular matrix, finely regulate skin mechanics via membrane‐bound ligands. Creating synthetic assemblies that mimic fibroblast function is appealing yet challenging. Here, we present a strategy that co‐assembles lignin with divinyl ligands to generate fibroblast‐mimicking polymersomes, enabling precise programming of bulk materials to emulate human skin across distinct physiological stages. Because lignin polymersomes are driven by relatively weak π–π stacking, hydrophobic ligands efficiently intercalate among aromatic rings, and their interfacial distribution can be tuned via molecular engineering. The polymersomes can be programmed in a Boolean logic‑gate manner (OR, AND, and NOT) to synthesize skin‐mimetic gels with tailored mechanical properties, analogous to fibroblast behavior. Furthermore, the platform enables on‑demand, high‑resolution 3D printing of complex bioskin architectures. This work provides a biomimetic paradigm for the synthesis and precise control over assembly from the molecular to the macroscopic scale.

KAN-PROSPECT: a Kolmogorov–Arnold Networks–integrated framework for predicting the effects and adverse reactions of natural products via transfer learning

Interfacial Electronic Modulation Redirects Anodic Radical Chemistry for Selective C─C Bond Cleavage in Electro‐Oxidative Lignin Depolymerization

ABSTRACT Electro‐oxidative lignin depolymerization is considered a promising route to renewable aromatics; however, its selectivity is often limited by competition with oxygen evolution and uncontrolled overoxidation at the anode. A CuO/Cu 0.92 Co 2.08 O 4 hetero structured catalyst was developed, with which 88% conversion of 2‐phenoxy‐1‐phenylethanol was achieved, affording benzaldehyde and phenol in 53% and 27% yields, respectively. By means of time‐resolved analysis and intermediate‐feeding experiments, a tandem pathway involving benzylic oxidation to 2‐phenoxyacetophenone followed by C α ‐C β scission was identified. In situ Raman and FTIR spectroscopy, together with EPR, revealed that the Cu─Co interface suppresses the accumulation of OER‐type CoOOH species while promoting oxygen‐centered radical chemistry under reaction conditions. Through density functional theory, it was further shown that interfacial electronic modulation strengthens substrate adsorption and lowers the barrier for bond cleavage. The same mechanistic logic was extended from the model substrate to enzymatic hydrolysis lignin, for which characteristic interunit linkages are weakened while aromatic products are retained. These findings establish interfacial control of anodic radical chemistry as a strategy for selective lignin bond editing under electrochemical conditions.

Sporulation generates stress-dependent phenotypic variability in the heterothallic industrial Saccharomyces cerevisiae Ethanol Red

Beyond Fluorination: A Golden Criterion Guided by Chemical Coordination‐Informed Machine Learning for High‐Voltage Electrolyte Design

ABSTRACT Fluorine chemistry has garnered attention for extending operating voltage limits of electrolytes through robust interfacial passivation owing to fluorine's strong electronegativity. However, confronted with solvent/salt/additive multicomponent induced vast combinatorial space, conventional high‐voltage electrolyte recipe design has been confined to reliance on fluorine content adjustments, resulting in inevitable trade‐off between oxidation stability and ion transport kinetics. Herein, we develop a Chemical Coordination‐Informed Molarity feature parsing approach embedded into machine learning for training adapted models. By building the one‐to‐one mapping between components and chemical‐coordination atomic molarities of a given recipe, the trained gradient boosting regression achieves a prediction of oxidation potential with MAE below 0.36 V. Demonstrating 2808 experiment operational candidates based on a ternary‐solvent blend, we reveal the pronounced role of mono‐coordinated fluorine and double‐bonded oxygen molarity ratio (F1/O1) for breaking the oxidative stability limit, and define a golden design criterion for guiding O1‐involved recipes: F1(≥8.19)/O1(≥13.39)[0.55, 1.10]. Following this, we validate three experimentally reported low‐fluoride recipes and identify two promising ones exhibiting oxidation potentials around 6.3 V vs. Li + /Li along with high ion‐transport kinetics for further assessments. This work demonstrates customizable feature engineering in yielding intelligent materials design principles for reconciling multiple target performance that are usually mutually exclusive.

Weighted knowledge distillation for semi-supervised segmentation of maxillary sinus in panoramic X-ray images

DS-ARO: a multi-strategy improved artificial rabbits optimization algorithm for global optimization and corporate bankruptcy prediction

A customized AI-based machine learning model for evaluating participants’ activities under a workshop setting

Adaptive queue management in healthcare using supervised Q-learning with time-varying reward and cost structures

Advancing biomedical data analytics using explainable neural network-based learning model for progressive neurodegenerative disorder diagnosis

Abstract Huntington’s disease (HD) is an inherited neurological disease caused by variations in the huntingtin (HTT) gene, which leads to neuronal degeneration. Conventionally, HD is affiliated with the gathering and misfolding of mutant HTT arising from an increased number of CAG triplets. Artificial Intelligence has emerged as an important tool in healthcare, supporting the monitoring, detection, and management of HD. Machine learning and deep learning methods are widely used for automated HD identification using neuroimaging, genetic, and clinical data. However, most DL models behave like a black box, making it difficult to interpret decision-making from clinical data, which reduces trust in medical applications. Therefore, this study presents an Explainable Neural Network-Driven Learning Model for Neurodegenerative Disorder Diagnosis (XNNLM-NDD). The primary objective of the proposed model is to examine clinical attributes and identify disease patterns efficiently for precise diagnosis. The model performs feature selection using a hybrid combination of minimum redundancy maximum relevance and ReliefF methods to select the most informative and non-redundant features from the dataset. For classification, the proposed approach employs a feature tokenizer-transformer model, which can capture complex feature interactions and improve classification accuracy on structured medical data. Furthermore, the model is optimized using the Cycle-Norm-Adam algorithm. For ensuring model transparency and interpretability, SHAP-based explainable artificial intelligence method is used to highlight the contribution of each feature towards the final prediction. The experimental evaluation is carried out on the Huntington Disease Dataset sourced from Kaggle. The results show that the proposed XNNLM-NDD approach accomplishes improved performance with an accuracy of 96.50% compared to existing techniques, indicating its efficiency in progressive neurodegenerative disorder diagnosis.

Prophage genomics of carbapenemase-producing Klebsiella pneumoniae from animal-derived food sources

Perceived and received support among patients with cancer receiving palliative care in Malta: a qualitative study

MST1/Drp1 axis mediates microglia pro-inflammatory activation following cerebral ischemia-reperfusion injury

Abstract The present study investigated the role of the mammalian sterile 20-like kinase 1/dynamin-related protein 1 (MST1/Drp1) axis in regulating microglia pro-inflammatory activation during cerebral ischemia-reperfusion injury (CIRI). An in vivo model of middle cerebral artery occlusion/reperfusion (MCAO/R) in rats and an in vitro oxygen-glucose deprivation/reoxygenation (OGD/R) model in BV-2 microglial cells and primary microglia were established. Inhibitors of MST1 (XMU-MP-1) or/and Drp1 (Mdivi-1), along with genetic approaches including siMST1-mediated knockdown and plasmid-based overexpression, were utilized in the models. The expression and activation of MST1 and Drp1, mitochondrial morphology changes, microglia pro-inflammatory activation makers, pro-inflammatory cytokine release, DNA fragmentation and neurological function were evaluated. The findings indicated that reperfusion or reoxygenation led to a rise in total and phosphorylation levels of MST1 and Drp1. The reperfusion also facilitated the Drp1 translocation toward mitochondria, and resulted in increased mitochondrial morphological changes. MST1 or/and Drp1 inhibitors decreased p-MST1 and p-Drp1(Ser616) levels, attenuated mitochondrial fission, suppressed microglia pro-inflammatory activation, pro-inflammatory factors release (TNF-α, IL-6 and IL-1β). Overall, these effects ultimately mitigated cerebral injury as evidenced by reduced DNA fragmentation, decreased cerebral infarct volumes, and improved neurological function. Combined inhibitors further exerted ameliorative effects on the above-mentioned parameters. In the in vitro experiments, siMST1 knockdown attenuated p-Drp1(Ser616) expression and suppressed microglia pro-inflammatory activation under OGD/R conditions. These protective effects were reversed by Drp1 overexpression. These findings indicate that p-MST1 drives microglia pro-inflammatory activation via promoting the p-Drp1(Ser616)-mediated excessive mitochondrial fission during CIRI.

AI‑driven framework for contract risk automation and compliance in oracle CPQ

Leveraging CT-derived chronic imaging signatures for acute kidney injury evaluation

Genetic profiling enhances cystic fibrosis prenatal diagnosis

Molecular evidence for early deuterostome origins of ovarian cell types and neuroendocrine control of reproduction

Automatic pain assessment from facial action units in ICU patients via various machine learning models

Abstract Pain represents a critical vital sign monitored in intensive care unit (ICU) patients. The facial action coding system (FACS) defines facial action units (AUs) and provides a structured framework for pain recognition. Currently, the most broadly used pain assessment method is the pain intensity scale developed by Prkachin and Solomon (PSPI), which relies on predefined AUs to quantify facial expressions. However, due to the influence of underlying diseases and facial texture variations in ICU patients, AUs can fail to transfer to clinical settings accurately. To address this problem, this study uses video sequences of pain states collected from 61 ICU patients under resting, daily, and procedural conditions by using an advanced AU detection system. By evaluating the AUs with statistical features and various classification models, this study identifies six key AUs that outperform the PSPI’s predefined AUs in terms of accuracy, precision, recall, and F1-score metrics. Further, this study explores the performance of various temporal self-learning networks in the pain assessment task, thus further validating the effectiveness of the identified AU combination. The results presented in this study demonstrate that using AU dynamic learning in combination with deep temporal analysis can improve the reliability of clinical pain assessment. Finally, this study offers a promising approach for automated pain monitoring systems in ICU settings.

In situ reprogramming of CAR-alveolar macrophages via liposomal nanomedicine for lung cancer immunotherapy

Multifunctional bioactivity of Portulaca oleracea seed oil: GC-MS-based characterization and in vitro evaluation

Abstract Purslane ( Portulaca oleracea L.) is a widespread weed highly valued for its nutritional value, particularly its omega -3 fatty acid content. This study aimed to investigate the pharmacological properties and the phenolic and flavonoid contents of P. olec racea seed oil. P. oleracea seed oil was tested for antioxidant activity (DPPH· and ABTS), cell viability and cytotoxicity and anti-inflammatory activity. The contents of total phenolics, flavonoids, and constituents were characterized. The seed oil of P.oleracea had high flavonoid (4.53 mg/g) and phenolic (8.65 mg/g) content. The antioxidant activity of P. oleracea seed oil increased from 24.16% at a concentration of 0.5 µg/mL to 97.28% at a concentration of 1000 µg/mL. The MTT assay demonstrated that P. oleracea seed oil exhibited relatively weak to moderate cytotoxic activity against the A549, HepG-2, and MCF-7 cell lines. Lower concentrations of the seed oil extract exhibited anti-inflammatory activity, and these effects were compared with those of indomethacin as a positive control. GC-MS analysis showed that the seed oil of P. oleracea is rich in olean-12-ene-3,28-diol and 9-octadecenoic acid. It could be concluded that P. oleracea seed oil exhibits notable in vitro antioxidant and cytotoxic activities, which may be attributed to its content of flavonoids and phenolic compounds. However, further in vivo studies and toxicity evaluations are required to confirm its potential applications.