A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells.

We present multimodal confocal Raman micro-spectroscopy (RS) and tomographic phase microscopy (TPM) for quick morpho-chemical phenotyping of human breast cancer cells (MDA-MB-231). Leveraging the non-perturbative nature of these advanced microscopy techniques, we captured detailed morpho-molecular data from living, label-free cells in their native physiological environment. Human bias-free data processing pipelines were developed to analyze hyperspectral Raman images (spanning Raman modes from 600 cm to 1800 cm, which uniquely characterize a wide range of molecular bonds and subcellular structures), as well as morphological data from three-dimensional refractive index tomograms (providing measurements of cell volume, surface area, footprint, and sphericity at nanometer resolution, alongside dry mass and density).

Impact of Digital Supply Chain Finance to alleviate SMEs financing constraints: Evidence from SRDI enterprises in China.

To explore the alleviating effect of digital supply chain finance (DSCF) on financing constraints experienced by small- and medium-sized enterprises (SMEs), with a view to promoting the digital transformation of enterprises. This observational study utilizes data from Chinese listed enterprises. The study's primary focus is on a selection of SRDI (abbreviation for "specialized, refined, distinctive, and innovative") enterprises in the electronics and machinery industries from 2013 to 2020.

Mixed-Length Multivariate Covalent Organic Framework for Combined Near-Infrared Photodynamic Therapy and Drug Delivery.

Covalent organic frameworks (COFs) have been emerging as versatile reticular materials due to their tunable structures and functionalities, enabled by precise molecular engineering at the atomic level. While the integration of multiple components into COFs has substantially expanded their structural complexity, the strategic engineering of diverse functionalities within a single framework the random distribution of linkers with varying lengths remains largely unexplored. Here, we report a series of highly crystalline mixed-length multivariate COFs synthesized using azobenzene and bipyridine as linkers, where tuning the ratio of linkers and incorporating palladium effectively modulates the balance between near-infrared (NIR) light absorption and catalytic sites for NIR-generation of hydrogen peroxide (HO).

Molecular Hybrid Bridging for Efficient and Stable Inverted Perovskite Solar Cells without a Pre-Deposited Hole Transporting Layer.

Establishing a low-resistance perovskite/ITO contact using self-assembled molecules (SAMs) is crucial for efficient hole transport in perovskite solar cells (PSCs) without a pre-deposited hole-transporting layer. However, SAMs at the buried interface often encounter issues like nonuniform distribution and molecular aggregation during the extrusion process, leading to significant energy loss. Herein, a molecular hybrid bridging strategy by incorporating a novel small molecule is proposed, (2-aminothiazole-4-yl)acetic acid (ATAA), featuring a thiazole ring and carboxylic acid group, along with the commonly used SAM, 4-(2,7-dibromo-9,9-dimethylacridin-10(9H)-yl)butyl)phosphonic acid (DMAcPA), into the perovskite precursor to synergistically optimize the buried interface.

Enzymatic Anisotropic Growth of Gold Nanoparticles Based on DNA Origami Templates.

Anisotropic gold nanoparticles (AuNPs) exhibit unique physicochemical properties that render them highly valuable for diverse applications. However, precise control over their growth direction and number of branches is challenging with conventional synthesis methods. A DNA origami-templated enzymatic synthesis strategy addresses this limitation.

Iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL-IQ) for evaluation of early bone mass changes in ageing osteoporosis patients.

This study explored the application value of iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL-IQ) technology in the early diagnosis of ageing osteoporosis (OP). 172 participants were enrolled and underwent magnetic resonance imaging (MRI) examinations on a 3.0T scanner.

A drug response prediction method for single-cell tumors combining attention networks and transfer learning.

Introduction: Accurately predicting tumor cell line responses to therapeutic drugs is essential for personalized cancer treatment. Current methods using bulk cell data fail to fully capture tumor heterogeneity and the complex mechanisms underlying treatment responses.

Methods: This study introduces a novel method, ATSDP-NET (Attention-based Transfer Learning for Enhanced Single-cell Drug Response Prediction), which combines bulk and single-cell data.

Experimental quantum-enhanced kernel-based machine learning on a photonic processor.

Recently, machine learning has had remarkable impact in scientific to everyday-life applications. However, complex tasks often require the consumption of unfeasible amounts of energy and computational power. Quantum computation may lower such requirements, although it is unclear whether enhancements are reachable with current technologies.

Fluorescent Self-Healing Elastomers with Triple Dynamic Bonds for 2D/3D Printed Information Encryption.

The development of novel optical self-healing materials holds significant importance for applications in anticounterfeiting and information encryption, but remains a formidable challenge. This study presents a fluorescent self-healing material designed for 2D/3D printing anticounterfeiting applications, exhibiting outstanding properties such as high transmittance, excellent mechanical strength, and remarkable self-healing efficiency. The triple dynamic bond networks provide robust mechanical and self-healing capabilities, with the polymer demonstrating a tensile strength of 26.

Economic Assessment of Bio-oil Production from Algae: A Bibliometric Analysis.

Introduction: The production of bio-oil from microalgae is gaining attention as an alternative renewable energy source. To generate advances in this field, it is essential to identify the gaps in existing research. Overcoming this barrier necessitates addressing methodologies that can assess the existing work and develop relationships between publications, research groups, and their impact.

Controllable synthesis of -hexagonal ZnAl-LDHs nanosheets for high-performance room-temperature ethanol gas sensing.

Layered double hydroxides (LDHs) have attracted considerable attention in gas sensing applications due to their highly tunable chemical composition and unique two-dimensional layered architecture. In this study, a series of ZnAl-LDHs with varying Zn/Al molar ratios were synthesized a facile hydrothermal method, and their ethanol sensing performance at room temperature was systematically evaluated. The influence of composition on the structural, morphological, and electronic properties of the materials was thoroughly investigated using a suite of characterization techniques, including XRD, FTIR, SEM, TEM, BET, XPS, PL, and EPR.

Catalysis of Radical Coupling Reaction via Synergistic Action of Oriented External Electric Field and Light Irradiation.

Radical coupling reactions have been widely used in the synthesis of complex organic molecules, materials science, and drug research. However, restricted conditions or special catalysts are required to overcome the energy barrier and trigger the coupling reaction efficiently. In this study, we provide experimental evidence that the C─N radical coupling reactions can be significantly accelerated by an oriented external electric field (OEEF) under synchronous UV irradiation without a catalyst.

Acetone Gas Sensors for Noninvasive Diabetes Diagnosis: A Comprehensive Review.

The development of sensors for monitoring breath acetone, a key biomarker for ketosis in diabetes mellitus, represents a critical frontier in medical diagnostics, promising a painless alternative to invasive blood tests. This review provides a comprehensive and critical evaluation of the state-of-the-art in acetone gas sensing technologies, including chemiresistive, optical, electrochemical, conductometric, and microwave platforms. We focus specifically on recent breakthroughs driven by advanced materials, analyzing how novel nanostructures from two-dimensional (2D) materials such as MXenes to porous metal-organic frameworks (MOFs) are engineered to push performance to clinically relevant parts-per-billion (ppb) sensitivity.

Gbits/s-Level Encrypted Spectral Wireless Communication Enabled by High-Performance Flexible Organic Hyperspectrometer.

The exponential growth of data in the information era has pushed conventional optical communication technology to its limitations, including inefficient spectral utilization, slow data rate, and inherent security vulnerabilities. Here, a transformative high-speed organic spectral wireless communication (SWC) technology enabled by a flexible, miniaturized, and high-performance organic hyperspectrometer is proposed that integrates ultrahigh-speed data transmission with hardware-level encryption. By synergistically combining organic photodetector arrays with tunable responsivities and spectral-tunable organic filters, the organic hyperspectrometer achieves a broad spectral detection range of 400 to 900 nm, resolution of 1.

Multi-scale convolutional attention GRU network combined with improved CARS strategy to determine key elements in ores by XRF.

Background: X-ray fluorescence (XRF) technology is a promising method for estimating the metal element content in ores, which helps in understanding ore composition and optimizing mining and processing strategies. However, due to the presence of a large number of redundant features in XRF spectra, traditional quantitative analysis models struggle to effectively capture the nonlinear relationship between element concentration and spectral information of XRF, making it more difficult to accurately predict metal element concentrations. Thus, analyzing ore element concentrations by XRF remains a significant challenge.

Enhancing D retention index accuracy: Correcting D retention time shifts in GC×GC due to modulation timing deviations via line detection technology.

Even small deviations in modulation timing (DMT) can cause discrepancies between the originally set modulation period (P) and the actual P in comprehensive two-dimensional gas chromatography (GC × GC) analysis. This study explored the impact of DMT on the accuracy of the second dimensional retention time (t) and the second dimensional retention index (I) calculations and introduced a line detection technology (LDT) based on the Hough Transform to correct DMT-induced t shifts. The correction was achieved by automatically adjusting candidate P values until the slope of the line(s) (CB_line(s)) formed by column bleeding compound peaks in the isothermal section of the contour plot approached zero, thereby determining the actual P.

Quantitative Susceptibility Mapping for Evaluating Renal Functional Injury in Chronic Kidney Disease Patients: A Pilot Study.

Chronic kidney disease (CKD) is an increasing global health problem, resulting in gradual loss of renal function and irreversible renal injury. The noninvasive detection, monitoring, and timely intervention of CKD might benefit the patients' prognosis. This study aims to assess renal functional injury in CKD patients by using magnetic resonance imaging (MRI) of quantitative susceptibility mapping (QSM).

Assessment of Hospital Resilience in Response to Medical Surges During Major Emerging Infectious Diseases: A Cross-Sectional Study from China.

Objective: This study aimed to systematically assess hospital resilience of China and identify critical contributing elements using a score-based evaluation and network analysis approach.

Methods: A cross-sectional observational study was conducted in hospitals from April to October 2024, involving 2,084 medical personnel. Sociodemographic data were collected using self-reported questionnaires.

Discovery of Potential GPRC5D Inhibitors through Virtual Screening and Molecular Dynamics Simulations.

G protein-coupled receptor family C, group 5, member D (GPRC5D), a member of the G protein-coupled receptor (GPCR) family, has recently emerged as a promising target for immunotherapy in hematologic malignancies, particularly multiple myeloma. However, no systematic virtual screening studies have been conducted to identify small-molecule inhibitors targeting GPRC5D. To address this gap, a multistep computational screening strategy is developed that integrates Protein-Ligand Affinity prediction NETwork (PLANET), a GPU-accelerated version of AutoDock Vina (Vina-GPU), molecular mechanics/generalized born surface area (MM/GBSA), and an online tool for Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) property prediction (admetSAR 3.

Developing Community-Informed Communication Resources to Improve Human Papillomavirus Vaccine Uptake in Tonga.

Introduction: Despite high coverage of routine childhood vaccines, uptake of the human papillomavirus (HPV) vaccine in the Pacific Island nation of Tonga has been slow. Culturally appropriate communication resources on the importance, safety, and effectiveness of the HPV vaccine are critical to support acceptance and uptake. To develop these resources, it is important to understand what people want to know.

CDLR-net: a ECG classification network based on deep residual shrinkage networks and LSTM.

Many traditional classification networks directly use the limb two-lead signal (MLII) ECG signals as input for training. However, this method suffers from reduced accuracy when ECG features are not obvious, especially for premature heartbeats. To solve the issue, this paper proposed a novel network, namely CDLR-Net, that combines a Deep Residual Shrinkage Network (DRSN) with a Long Short-Term Memory (LSTM).

Capturing G protein-coupled receptors into native lipid-bilayer nanodiscs using new diisobutylene/maleic acid (DIBMA) copolymers.

Many membrane proteins, including G protein-coupled receptors (GPCRs), are susceptible to denaturation when extracted from their native membrane by detergents. Therefore, alternative methods have been developed, including amphiphilic copolymers that enable the direct extraction of functional membrane proteins along with their surrounding lipids. Among these amphiphilic copolymers, styrene/maleic acid (SMA) and diisobutylene/maleic acid (DIBMA) polymers have been extensively studied.

Ortho-bridge system for the treatment of Vancouver type B1 periprosthetic femoral fractures based on three dimensional printing.

Objective: This study aims to evaluate the clinical outcomes of Vancouver B1 periprosthetic femoral fractures (PFF) treated with the Ortho-bridge system (OBS) internal fixation and assess the potential benefits of 3D printing technology in preoperative planning and surgical execution for these cases.

Method: This retrospective study analyzed 55 consecutive Vancouver B1 periprosthetic femoral fracture cases treated surgically at Yan'an Affiliated Hospital of Kunming Medical University (2014-2022) with minimum 1-year follow-up. Patients were divided into conventional ORIF (n = 21) and OBS fixation groups (n = 34), with the OBS group further stratified into standard procedure (n = 18) and 3D-printing-assisted (n = 16) subgroups.

Boosting the Efficiency of 1.84 eV Wide-Bandgap Perovskites Photovoltaics Beyond 19% via Yb Engineering.

Phase segregation remains one of the most critical challenges limiting the performance and long-term operational stability of wide-bandgap perovskite solar cells (PSCs). This issue is especially pronounced in 1.84 eV wide-bandgap (WBG) perovskites, where severe halide phase segregation leads to compositional heterogeneity and accelerated device degradation.

Cognitive prediction using regional connectivities and network biomarkers in Alzheimer's disease.

Achieving a deep understanding of brain mechanisms requires multi-scale perspectives to capture the architecture of complex networks. In this study, we focused on patients with cognitive impairment and constructed individual brain networks from neuroimaging data. We introduced a Significant Edges Selection (SES) method, which effectively extracts the most informative connections while suppressing noise.