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ATP‐Powered Signaling Between Artificial and Living Cells

Abstract ATP is the energy currency of life and is overabundant in the tumor microenvironment, where it has been suggested as a target for cancer therapy. We introduce ATP‐dissipative delivery of DNA signals from artificial cells to living cells by exploiting an ATP‐driven reaction network that transiently ejects DNA Signal strands from the shielded artificial cell interior to the extracellular medium of living cells. We customize the Signal for intracellular uptake or for extracellular instruction using a cytokine‐ssDNA chimera that can trigger efficient intracellular downstream signaling programs. Our study discusses details of system design on a timer circuit and artificial cell level, system integration challenges, and how ATP concentrations regulate the transient delivery. The strategy can be extended to deliver therapeutic oligonucleotides for applications in gene therapy and gene silencing. For cancer therapy, it can use naturally enhanced ATP levels to induce selective delivery of therapeutic oligonucleotides.

Adaptive bandit algorithms increase efficiency of mobile tuberculosis screening programs

Enzyme Immobilization in Porous Crystals via Cage Encapsulation

Abstract Enzyme immobilization within solid or polymer matrices continues to face challenges, such as inhomogeneous protein environments, low loading efficiency, and limited versatility. Here, we present a pre‐encapsulation strategy for enzyme immobilization in porous crystals via coordination cage encapsulation. In this approach, enzymes are first confined within the well‐defined cavities of spherical coordination cages prior to crystallization. The host cage framework facilitates crystal formation under almost uniform conditions, independently of the surface properties of the enzymes. This isomorphous crystallization enabled the successful immobilization of nine proteins with diverse sizes and properties, achieving high loading efficiencies. The immobilized enzymes retained their native structures and catalytic activities within the crystal, functioning as heterogeneous catalysts with enhanced stability. Furthermore, the co‐immobilization of two enzymes in close proximity facilitated cascade reactions, underscoring the potential of coordination cages as effective hosts for enzyme immobilization.

Boric acid-functionalized bentonite nanostructures attenuated paraquat-induced pulmonary injuries in rats

Surface treatments and coatings of hybrid glass ionomer cement to improve mechanical and physical properties

Abstract This in vitro study evaluated the flexural strength, fluoride release, water sorption, and solubility of a high-viscosity hybrid glass ionomer cement (HVGIC, Equia Forte HT, GC Europe) over 28 days following six surface treatments: Equia Forte Coat (light-cured, 20s), bonding agent (Clearfil SE Universal Bond, 20s), light-curing alone as thermal treatment (20s or 60s), petroleum jelly, and untreated control. Specimens were stored in artificial saliva at 37 °C. Flexural strength (three-point bend test, ISO 4049:2019) and fluoride release were assessed at 24, 48, 96 h and 28 days. Sorption and solubility were measured at 28 days. Statistical analysis included bivariate tests, Kaplan-Meier survival curves, Tukey’s post-hoc, and Weibull regression. Proprietary coat and bonding agent reached the minimum required strength (MRS = 80 MPa) fastest (< 2 days), followed by petroleum jelly (2.5 days), 60s light curing (3 days), 20s (4 days), and control (5.5 days). Control showed the highest fluoride release initially, while at 28 days, 60s light-curing released the most fluoride. Proprietary coat and bonding agent showed minimal release. No significant differences in water sorption or solubility were found. These findings suggest that specific coatings or prolonged light curing can improve HVGIC performance and longevity of restorations.

Author Correction: Early social adversity modulates the relation between attention biases and socioemotional behaviour in juvenile macaques

Contrasting pre-vaccine COVID-19 waves in Italy through functional data analysis

Controllable diffusion framework for imbalanced Phi OTDR events classification

Atroposelective Synthesis of Ultrarigid Chiral Styrenes via Pd‐Catalyzed Unconventional <i>5‐exo‐dig</i> Cyclization/Carbonylation of 1,5‐Enynes

Abstract Axially chiral styrenes represent a promising class of atropisomers with broad potential in asymmetric catalysis, drug discovery, and materials science. However, their catalytic asymmetric synthesis remains underdeveloped due to the conformational flexibility and inherently low rotational barriers. Herein, we report a Pd‐catalyzed cyclization/carbonylation of 1,5‐enynes that enables efficient access to a series of configurationally ultrastable axially chiral styrenes in good to high yields (up to 88%) with excellent enantioselectivities (up to 99.5/0.5 er). Key to success is the strategic design of an ortho ‐alkyne‐substituted styrene substrate that favors olefin‐first Pd–hydride insertion, followed by unconventional 5‐exo‐dig cyclization and carbonylation. This protocol exhibits broad nucleophile compatibility, including alcohols, water, and amines, enabling the modular synthesis of diverse chiral styrene‐based esters, carboxylic acids, and amides. The resulting conformationally rigid product was further transformed into an axially chiral monophosphine ligand, which demonstrated high efficiency in asymmetric catalysis. Mechanistic investigations support a Pd–hydride insertion–cyclization–carbonylation sequence. This transformation provides a powerful platform for the asymmetric synthesis and application of axially chiral styrenes.

Spatial distribution of microplastics in Persian Gulf marine environments

Abstract Microplastics (MPs) are recognized as a significant environmental threat within marine ecosystems. This study examines the spatial distribution of microplastics in seawater, sediments, and fish from the Persian Gulf. A total of 24 seawater samples, 24 sediment samples, and 40 specimens of Pennahia anea were collected from eight locations. Samples were digested, and MPs were filtered, counted, and analyzed using a stereo microscope, Raman spectroscopy, and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM/EDX). The results revealed the presence of microplastics across all sample types. The total abundance of MPs in seawater ranged from 3 to 15 items/l, in sediments from 10 to 35 items/kg, and in fish from 4 to 18 items/10 g. The study identified a robust positive correlation between microplastic contamination in seawater and fish ( r  = 0.932, p  = 0.001). Similarly, a strong positive correlation was observed between sediment and fish contamination ( r  = 0.730, p  = 0.040). In seawater, sediment, and fish samples, microplastics were predominantly fibers, constituting 98.31%, 100%, and 87.5% of the total microplastic content, respectively. These fibers measured ≤ 250 μm in length, with black being the most prevalent colors. The findings highlight that the northern site, Khor Semaili (K1), was a significant MP hotspot. At the same time, areas such as Khor Zangi (K6) and Khor Ghazaleh (K8) exhibited minimal contamination across all matrices. This research highlights the crucial role of spatial analyses in understanding the distribution of microplastics in marine ecosystems.

Click‐Cyclized Cell Penetrating Peptides Containing Hydrophobic Proline Derivatives for Efficient Intracellular Delivery

Abstract Targeting intracellular organelles with macromolecules is challenging due to cell membrane impermeability. Innovative approaches to direct intracellular delivery have been developed, including nanoparticles, as a promising method for protein delivery; however, with limited efficiency. Cell‐penetrating peptides (CPPs) provide a versatile alternative for intracellular cargo delivery. We suggest substituting hydroxy‐ or amino prolines in β‐turns in cyclic CPPs with hydrophobic residues to facilitate cell penetration. We investigated the influence of substituted amine and ether derivatives of trans ‐4‐hydroxy‐ or amino‐ l ‐proline on MCF‐7 and other cell lines for the import of the fluorophore ATOTA. Flow cytometry and spinning disk confocal microscopy on 17 linear and click‐cyclic peptides revealed that particularly cyclic cell penetrating peptide 15 (cCPP 15 ) exhibited remarkable permeability. When conjugated with enhanced green fluorescent protein (EGFP), cCPP 15 efficiently delivered the protein into MCF‐7 and HeLa cells. cCPP 15, comprising two endocyclic and two exocyclic d ‐arginines and a trans amino‐L‐proline with a 2‐naphthylmethylene modification in a typeIIβ‐turn, significantly enhanced intracellular penetration. The efficiency of cCPP 15 in intracellular delivery provides a valuable addition to the growing field of CPPs that can deliver macromolecules to the interior of the cell and possibly even cross the blood‐brain barrier with proteins or drugs.

Rumen microbiome profiles of dairy cattle are affected by the presence of, and vaccination against, the abomasal parasitic nematode Ostertagia ostertagi

Steroid therapy is linked to lower incidence of acute kidney injury in patients with severe alcohol-associated hepatitis

Abstract Acute kidney injury (AKI) is a common complication in patients with severe alcohol-associated hepatitis (sAH), yet its risk factors and outcomes remain incompletely defined. Corticosteroids are the only established therapy for sAH, but their impact on renal outcomes is unknown. A post-hoc analysis of the prospective multicenter VTL-308 trial including 151 patients with sAH was conducted to evaluate the incidence and predictors of AKI, the association of corticosteroid therapy with AKI risk, and factors associated with AKI reversal during 90 days of follow-up. AKI was independently associated with significantly higher 90-day mortality (subdistribution hazard ratio (sHR) = 8.74; p &lt; 0.001). In multivariate competing risk analysis, higher bilirubin levels (sHR = 1.06; p = 0.003) were linked to increased AKI risk, while corticosteroids were associated with an ameliorated AKI risk (sHR = 0.47; p = 0.01). Time-censored analyses confirmed the protective association of corticosteroids (HR = 0.25; p = 0.001). Once AKI occurred, corticosteroids did not promote renal recovery (HR = 1.15; p = 0.74). Elevated bilirubin levels were linked to a lower probability of AKI reversal. In conclusion, AKI is a devastating complication in sAH. Corticosteroid therapy is independently associated with a significantly lower risk of AKI development but does not influence recovery once AKI has occurred. Future prospective trials are required to provide further validation of these findings.

Optimization of automatic generation controllers in renewable multi-area power systems using the Fata Morgana algorithm

Abstract The increasing integration of renewable energy sources introduces severe intermittency in multi-area power systems (MAPS), resulting in significant voltage and frequency fluctuations. This study addresses this problem by implementing an automatic generation control (AGC) framework for a two-area hybrid power system composed of solar, wind, and thermal units. Four types of controllers (PI, PIDn, fractional-order PI (FOPI), and predictive PIDn (PPIDn)) were optimized using four recent metaheuristic algorithms: golden jackal optimization (GJO), educational competition optimizer (ECO), escape algorithm (ESC), and the newly proposed Fata Morgana Algorithm (FATA). The results demonstrate that the FATA-optimized PIDn controller provides the best dynamic performance, achieving an ITAE value of 0.18676, which represents an improvement of over 4.6% compared to the best established optimizer (ESC). Real-time validation on the OPAL-RT OP5707 platform confirmed the practical feasibility of the proposed FATA-based control strategy, verifying its ability to enhance frequency stability. These findings highlight the novelty and efficiency of FATA in optimizing AGC parameters for renewable-based multi-area power systems.

Artificial intelligence model as a tool to predict prediabetes

Abstract Prediabetes is characterized by elevated blood glucose levels that are higher than normal but below the threshold for diabetes mellitus. While AI models have been used for prediabetes prediction, most rely solely on standard clinical and biochemical markers. This study introduces a novel Pattern Neural Network (PNN) model that uniquely integrates total antioxidant scavenging potential with traditional risk factors, providing new insights into the role of oxidative stress in prediabetes risk stratification among Indian adults. A total of 199 individuals aged 18 to 60 years were recruited and classified based on HbA1c levels into Control ( n  = 99) and Prediabetes ( n  = 100) groups. Fourteen input features including age, gender, total antioxidant status, HbA1c, FBG, OGTT, TGL, HDL, LDL, VLDL, TC, WC, Hb, and BMI were used to train a PNN with 14 input nodes, 10 hidden nodes, and one output node. The dataset was randomly divided into training, validation, and testing subsets. Model performance was compared against SVM, KNN, and LR classifiers. Feature importance analysis was conducted to interpret the model’s clinical relevance. The PNN model achieved superior validation performance with an accuracy of 98.3%, outperforming SVM (96%), KNN (83%), and LR (71%). Notably, antioxidant scavenging potential and waist circumference emerged as the most influential predictors. The model’s output value of 0.8770 (threshold &gt; 0.5) effectively identified individuals at increased risk of developing diabetes. By incorporating oxidative stress markers, this study provides the first AI model in an Indian cohort to link antioxidant status to prediabetes risk. The PNN model demonstrates excellent accuracy and interpretability, offering a clinically actionable tool for early disease detection and personalized intervention.

Classifying human vs. AI text with machine learning and explainable transformer models

Abstract The rapid proliferation of AI-generated text from models such as ChatGPT-3.5 and ChatGPT-4 has raised critical challenges in verifying content authenticity and ensuring ethical use of language technologies. This study presents a comprehensive framework for distinguishing between human-written and GPT-generated text using a combination of machine learning, sequential deep learning, and transformer-based models. A balanced dataset of 20,000 samples was compiled, incorporating diverse linguistic and topical sources. Traditional algorithms and sequential architectures (LSTM, GRU, BiLSTM, BiGRU) were compared against advanced transformer models, including BERT, DistilBERT, ALBERT, and RoBERTa. Experimental findings revealed that RoBERTa achieved the highest performance (Accuracy = 96.1%), outperforming all baselines. Post-hoc temperature scaling (T = 1.476) improved calibration, while threshold tuning (t = 0.957) enhanced precision for high-stakes applications. McNemar’s test with Holm correction confirmed the statistical significance ( p  &lt; 0.05) of RoBERTa’s superiority. Efficiency analysis showed optimal trade-offs between accuracy and latency, and 20% pruning demonstrated sustainability potential. Furthermore, LIME and SHAP explainability analyses highlighted linguistic distinctions between AI-generated and human-authored text, and fine-grained error evaluation confirmed model robustness across text lengths. In conclusion, RoBERTa emerges as a reliable, interpretable, and computationally efficient model for detecting AI-generated content.

Flavonora: a new genus of lecanoroid lichens (Ascomycota, lichenized fungi)

Catalytic Non‐Oxidative C–C Coupling of Benzene to Biphenyl Over Sub‐Nanostructured Europium Hydride

Abstract Catalytic coupling of benzene to biphenyl through direct C─H bond activation and C─C bond construction represents an ideal, atom‐economical strategy, offering a streamlined alternative to classical multi‐step functionalization approaches, and thus has been a long‐sought after goal. Herein, we report that such a pathway can be realized over a sub‐nanostructured europium hydride (Eu–H) catalyst under mild condition. The Eu–H catalyst achieved a biphenyl production of 53.7 mmol g Eu −1 with a turnover number (TON) of 16.3 over 144 h at 150 °C. Mechanistic studies suggest that hydride species (H − ) in Eu–H preferentially deprotonate benzene to form a [C 6 H 5 –Eu] intermediate, which subsequently undergoes nucleophilic attack on another benzene molecule, resulting in biphenyl formation and regeneration of the Eu–H species to complete the catalytic cycle. This work not only provides a strategy for benzene coupling to biphenyl but also demonstrates the promising role of hydrides in transforming arenes into value‐added molecules.

Modifiable lifestyle factors and genetic risk of obesity in Indians

Coupling and coordination between the digital economy and carbon emissions in the Yangtze River Economic Belt