Fabrication and comprehensive experimental evaluation of surfactant-activated PEDOT:PSS/SnO thin films deposited via spin coating for advanced sensing applications.

This research investigates the fabrication of surfactant-mixed tin oxide (SnO) nanostructured thin films on a fluorine-doped tin oxide (FTO) substrate via hydrothermal synthesis, focusing on their structural, morphological, optical, and electrical properties for sensor applications. To examine the effect of surfactant concentration, cetyltrimethylammonium bromide (CTAB) was incorporated at varying weight percentages (0%, 6%, 11%, 16%, and 20%), resulting in five distinct sensor samples, labelled SnO-1, SnO-2, SnO-3, SnO-4, and SnO-5, respectively. X-Ray Diffraction (XRD) analysis confirms a tunable crystallite size from 12.

AI-driven reclassification of multiple sclerosis progression.

Multiple sclerosis (MS) affects 2.9 million people. Traditional classification of MS into distinct subtypes poorly reflects its pathobiology and has limited value for prognosticating disease evolution and treatment response, thereby hampering drug discovery.

A genome-wide study on gene-nutrient interactions for hyperuricemia in a large Korean cohort (KoGES).

This study aimed to identify novel genetic variants associated with hyperuricemia risk across multiple nutrients by assessing significant gene-nutrient interactions using large-scale genome-wide association study (GWAS) data in the Korean population. A total of 48,007 individuals from the Korean Genome and Epidemiology Study dataset were included in the GWAS. Dietary intake was evaluated using a food frequency questionnaire.

One Surface Fits All: Validating the Venous Return Model Across Species and Scales in Circulatory Equilibrium.

In the generalized framework of circulatory equilibrium, the cardiac output (CO), right (RAP) and left (LAP) atrial pressures at equilibrium are predicted from the intersection between the CO curve and venous return (VR) surface. The VR surface is represented by the following equation: VR=V/W-G∙RAP-G∙LAP, where V is stressed blood volume, and W, G, and G are parameters. For future clinical application of this framework, we examined whether the VR surface is valid, whether the parameters are allometrically scalable based on Kleiber's law, and whether the VR surface with allometrically scaled parameters accurately predicts hemodynamic variables across different animal species and a diverse range of body weights (BW).

The GGH/HuR Complex Binds and Stabilizes mRNAs to Maintain Tumor Cell Cycle and DNA Replication.

GGH (Gamma-glutamyl hydrolase) is a folate metabolism enzyme that hydrolyzes intracellular polyglutamylated folates and is highly expressed in various cancers. It remains unclear whether GGH functions as an oncogene and its underlying mechanisms in tumor progression. Here, it is reported that GGH silencing inhibited the growth of lung cancer cells in vivo and in vitro.

Analytical greenness metrics for metabolomics.

Background: Metabolomics is rapidly evolving, addressing analytical chemistry challenges in the qualification and quantitation of metabolites in extremely complex samples. Targeted metabolomics involves the extraction and analysis of target compounds, often present at extremely low concentrations, whilst untargeted metabolomics requires the use of sophisticated analytical techniques to deal with the simultaneous identification or quantitation of hundreds of compounds. Given the high energy consumption and excessive amounts of waste generated by metabolomics studies, greenness metrics are essential to account for sustainable development.

Current practices in the study of biomolecular condensates: a community comment.

The realization that the cell is abundantly compartmentalized into biomolecular condensates has opened new opportunities for understanding the physics and chemistry underlying many cellular processes, fundamentally changing the study of biology. The term biomolecular condensate refers to non-stoichiometric assemblies that are composed of multiple types of macromolecules in cells, occur through phase transitions, and can be investigated by using concepts from soft matter physics. As such, they are intimately related to aqueous two-phase systems and water-in-water emulsions.

Enhancing the accuracy of mental health assessments through the integration of self-report and objective measures: A convergence study utilizing biosignals and 14-day wearable data.

Traditional mental health assessments have primarily relied on self-report surveys. With the advancement of biosignal and daily life data acquisition technologies, there is growing potential to enhance the accuracy of mental health evaluations. This study examined the predictive value of self-reported and objective measures in assessing depressive symptoms and evaluated whether their integration improves model performance.

Convenient Production of Photothermal Recycling Phosphorescent Materials from Cellulose and Lignin.

Developing recyclable room-temperature phosphorescent (RTP) films using ultrafast fabrication techniques remains a critical yet challenging objective. With this research, we developed an RTP film (Cell-Lig) through ethanol-induced phase transition, achieving solid film formation within 1 s from ionic liquid (1-butyl-3-methylimidazolium chloride, [Bmim]Cl) solutions of cellulose and lignin. The phase transition also generated a confined rigid environment for Cell-Lig, activating thickness- and temperature-dependent green RTP emission from the incorporated lignin.

Asymmetric Charge Distribution of Dual Active Sites in Nitroaromatics Toward High-Performance Zinc Organic Batteries.

Organic small molecules have emerged as promising cathode candidates for aqueous zinc-ion batteries owing to their structural tunability and high redox activity. However, their development is hindered by inherently low operating voltages and limited specific capacities. Herein, a bipolar organic molecule is reported featuring intramolecular asymmetric charge distribution.

Targeted Delivery of mGluR5 Agonists via Engineered hucMSC-Derived EVs to Enhance Macrophage Function and Mitigate Glutamate-Induced Toxicity.

Extracellular vesicles (EVs) are emerging as a potential drug delivery system for neuroinflammatory diseases due to their ability to navigate natural barriers, maintain stability, and facilitate cell-cell signaling. However, their limited targeting ability often restricts their therapeutic efficacy, necessitating enhancements to enable precise targeting of crucial macrophage cells. To address this issue, we genetically modified EVs derived from human umbilical cord mesenchymal stem cells (hucMSCs) using a glycosylated pcDNA3.

The plasma p-tau217/BD-tau ratio improves biomarker short-term variability in memory clinic patients.

Introduction: Assessment of short-term intra- and inter-individual variability for Alzheimer's disease (AD) plasma biomarkers is essential for clinically relevant interpretation of biomarker levels. We hypothesized that the variability of plasma tau phosphorylated at threonine 217 (p-tau217) could be reduced by combining it with a tau marker, plasma brain-derived tau (BD-tau), as the p-tau217/BD-tau ratio.

Methods: Three consecutive blood samples were collected from memory clinic patients within 36 days.

Taurine alleviated paraquat-induced oxidative stress and gut-liver axis damage in weaned piglets by regulating the Nrf2/Keap1 and TLR4/NF-κB signaling pathways.

Background: Oxidative stress can impair intestinal barrier function and cause liver damage, resulting in reduced animal productivity. Paraquat (PQ) induces significant oxidative stress in weaned piglets. The antioxidant, anti-inflammatory, and metabolic regulatory functions of taurine (Tau), a free amino acid that is widely distributed in the body, have been extensively studied.

Development of a composite core collection from 5,856 Sesame accessions being conserved in the Indian National Genebank.

Objectives: A composite core collection (CCC) in sesame (Sesamum indicum L.) will help utilize genetic resources efficiently. This study reports, using genomics tools, a representative minimal set (CCC) that capture maximal genetic diversity from a set of 5,856 sesame accessions being conserved at the National Genebank (NGB) of the ICAR-NBPGR.

Ultraelastic Nanomembrane with Controllable Hotspots and Enhanced Photon Recovery for Single-Molecule Surface-Enhanced Raman Scattering.

The controlled assembly of noble metal nanomaterials at water-oil interfaces holds significant potential for enhancing the sensitivity and stability of surface-enhanced Raman scattering (SERS) technology. However, developing highly efficient nanomaterial assembly techniques to create structures with controllable plasmonic hotspots and easily transferable functionalities remains a critical challenge. Herein, we propose a hydrophobic modification-enhanced float assembly strategy to fabricate an ultraelastic nanomembrane (UE-nanomembrane) by reducing adsorption kinetic barriers.

Three-in-One π-Conjugated Chiral Macrocycles: Photoswitching, Amplified Chirality, and Photo-Tailored Chiroptics.

Chiral macrocycles have attracted considerable attention because of their fascinating circular chiral topologies, circularly distributed electronic structures, and excellent chiroptical features. However, the challenging synthesis and limited cases hinder the understanding, improvement, and regulation of their optoelectronic properties. To avoid this dilemma, we employed cyclic Suzuki-Miyaura coupling between dithienylethene (DTE) and [6]helicene to firstly synthesize donor-acceptor (D-A)-type π-conjugated chiral macrocycles, including m-[1 + 1], m-[2 + 2], and m-[3 + 3], where these compounds displayed photocontrollable ring sizes and chiroptical properties.

Case report of non-gene editing CD7 CAR T cell therapy in CD7 Sézary syndrome: preclinical validation and first-in-human use.

Introduction: Sézary syndrome (SS) is a leukemic form of cutaneous T cell lymphoma (CTCL), distinguished from mycosis fungoides by the presence of cancerous lymphocytes in the blood and often bears very poor prognoses. SS treatment is palliative, and thus novel therapies are needed. The CD7 surface antigen is highly expressed and confined to the surface of T cells, therefore when present, serves as a promising target for immunotherapy.

Cyantraniliprole-mediated transgenerational hormesis in the invasive tomato pinworm, Tuta absoluta.

Background: Hormesis, a biphasic dose-response phenomenon, is gaining recognition for its bioregulatory and practical implications. Invasive species present a critical context for studying hormesis, because sublethal population-level responses may facilitate establishment and spread, particularly when transgenerational effects are involved. The invasive South American tomato pinworm, Tuta absoluta Meyrick, a global agricultural pest, exemplifies this scenario.

Designing lipid nanoparticles using a transformer-based neural network.

The RNA medicine revolution has been spurred by lipid nanoparticles (LNPs). The effectiveness of an LNP is determined by its lipid components and their ratios; however, experimental optimization is laborious and does not explore the full design space. Computational approaches such as deep learning can be greatly beneficial, but the composite nature of LNPs limits the effectiveness of existing single molecule-based algorithms to LNPs.

Sustainable reduction of Cr (VI) by green synthesized chitosan coated copper (II) nanoparticles in batch reactor system: Isotherm, thermodynamics and kinetic study.

This study explores the use of green-synthesized chitosan-coated CuO nanoparticle (Ch-CuNs) for the sustainable removal of hexavalent chromium (Cr (VI)). Scanning electron microscope (SEM) analysis confirmed the formation of spherical nanoparticles, while energy dispersive X-ray analyzer (EDX) and elemental analysis verified the presence of Cr (VI) on the nanoparticle surface, indicating successful adsorption. Fourier Transform Infrared (FTIR) spectroscopy analysis identified functional groups on the nanoparticles that actively participated in Cr (VI) adsorption.

A generative deep learning approach to de novo antibiotic design.

The antimicrobial resistance crisis necessitates structurally distinct antibiotics. While deep learning approaches can identify antibacterial compounds from existing libraries, structural novelty remains limited. Here, we developed a generative artificial intelligence framework for designing de novo antibiotics through two approaches: a fragment-based method to comprehensively screen >10 chemical fragments in silico against Neisseria gonorrhoeae or Staphylococcus aureus, subsequently expanding promising fragments, and an unconstrained de novo compound generation, each using genetic algorithms and variational autoencoders.

Development of a Machine Learning Model for Aspyre Lung Blood: A New Assay for Rapid Detection of Actionable Variants From Plasma in Patients With Non-Small Cell Lung Cancer.

Purpose: Aspyre Lung is a targeted biomarker panel of 114 genomic variants across 11 guideline-recommended genes with simultaneous DNA and RNA for non-small cell lung cancer (NSCLC). In this study, we developed a machine learning algorithm to interpret fluorescence data outputs from Aspyre Lung, enabling the assay to be applied to both plasma and tissue samples.

Materials And Methods: Data for model training and testing were generated from over 13,500 DNA and RNA contrived samples, with variants spiked in at a variant allele frequency (VAF) of 0.

Application of Phage-Modified Conjugated Polymer-Based Nanoparticles in the Disruption of Bacterial Biofilms.

Photothermal antimicrobial therapy (PTT) has demonstrated significant potential in eradicating biofilms and reducing bacterial resistance. However, a major drawback of traditional PTT is the lack of selectivity, which reduces the efficiency and can cause substantial damage to healthy cells and tissues. Therefore, improving the precision of photochemical therapy against pathogenic bacteria is critical.

CGM Data Analysis 2.0: Functional Data Pattern Recognition and Artificial Intelligence Applications.

New methods of continuous glucose monitoring (CGM) data analysis are emerging that are valuable for interpreting CGM patterns and underlying metabolic physiology. These new methods use functional data analysis and artificial intelligence (AI), including machine learning (ML). Compared to traditional metrics for evaluating CGM tracing results (CGM Data Analysis 1.