Browse Articles

KNG-1, HSPG and Robo4 reflect hypertension-driven subclinical renal alterations and provide incremental diagnostic value in type 2 diabetic nephropathy

Abstract Diabetic nephropathy (DN), indicated by persistent urinary albumin excretion or albuminuria, is the major cause of end stage renal disease worldwide. Currently albuminuria is considered the best predictor of subsequent development of DN in type 2 diabetic patients, but it is often induced by arterial hypertension or heart failure in older patients as well. Therefore, additional biomarkers are needed to identify diabetic patients at a high risk of developing DN. The aim of the study was to investigate the expression profiles of some serum proteome biomarkers and to demonstrate their association with early renal alterations among type 2 diabetic patients. This study included 300 type 2 diabetic patients with- (200, UACR > 30 mg/gm) and without- (100, UACR < 30 mg/gm) persistent albuminuria in addition to 100 healthy control subjects. Serum expression patterns of KNG-1, HSPG, Robo4 and other biochemical parameters were evaluated in all participants. We also assessed the combined influence of hypertension and albuminuria on these markers, to explore their potential as non-invasive indicators of early renal risk in T2DM. Results suggest that elevated serum KNG-1, HSPG and Robo4 levels in type 2 diabetic patients might reflect a DN progressive pathology due to various mechanisms. The interaction between hypertension and albuminuria significantly alters biomarker levels in a synergistic manner, emphasizing the need for comprehensive management of both conditions in clinical practice. Additionally, findings highlight that serum proteome signature of these biomarkers provide an incremental predictive value when integrated with conventional clinical parameters, as evidenced by the marked improvement in multivariable regression model performance and discriminative accuracy analyses.

Messenger RNA-encoded reporters for monitoring cellular stress and bioenergetics

Abstract The ability to monitor cellular processes in real-time is essential for understanding cell function, disease progression, and therapeutic responses. Engineered reporter proteins have been developed for monitoring cellular metabolism, stress responses, and bioenergetics. However, their use in primary cells is limited by inefficient plasmid transfection and the impracticality of generating and validating stable cell lines for each application. Here, we use in vitro transcription to generate mRNA-encoded metabolic trackers and achieve high transfection efficiencies in primary fibroblasts, cancer cells, and induced pluripotent stem cells. This approach provides a flexible platform for real-time monitoring of cellular processes in diverse cell types and overcomes the technical barriers of establishing stable cell lines by genetic modification. We confirm the activity of three ratiometric reporters that monitor pH, H 2 O 2 , and ATP levels in subcellular compartments. Our mRNA-based approach provides a versatile, efficient tool for real-time metabolic studies across basic and applied research, reducing reliance on commercially available reporters and broadening the applicability of metabolic reporters in patient-derived cell models.

Mg ²⁺ Catalyzes Nonenzymatic RNA Primer Extension through a Concerted Outer-Sphere Mechanism

The impact of drought stress on the physiological biochemical indexes and metabolites in Panax notoginseng

Confidence-aware semi-supervised siamese graph networks for semantic text similarity in low-resource Urdu

Angle-Selective Optical Resonance and Circular Radial Lasing from a Chiral Polymeric Microsphere

An effect of silver arsenic sulfide and gallium sulfide dielectric materials in SPR refractive sensor: a numerical study

From Leuco to Blue: Photochemical Redox Amplification for Small-Molecule Immunodetection

An HEVC-based known-plaintext attack for video selective encryption

Activatable Immunotheranostic Nanovesicles for Coordinated Phagocytic Reprogramming and Real-Time Immune Monitoring

Multi-perspective prompt fusion for zero-shot classification of agricultural news texts with large language models

Enzyme-Catalyzed Stereoselective C(sp ³ )–S Bond Formation via a Dichotomic Carbene Transfer Mechanism

Stage-dependent prognostic impact of age in colorectal cancer: A population-based SEER analysis

Size of Biomolecular Condensates Dictates Fate in Liquid–Solid Phase Transitions through Amorphous–Amyloid Competition

High power density in a fully printed origami-inspired rolled thermoelectric generator enabled by paper self-folding

Abstract Thermoelectric generators (TEGs) can harvest waste heat for small electronics. However, printed planar TEGs are often limited by low thermocouple density within a given footprint. Here, we present a paper-based rolled thermoelectric generator (Rolled TEG) that autonomously transforms from a planar sheet into a cylindrical geometry via paper self-folding using a fully printed fabrication process. Silver nanoparticle ink is inkjet printed to form electrodes and interconnects, while PEDOT:PSS is screen printed to form p-type thermoelectric legs, enabling series-connected architectures on paper substrates. The self-folding transformation rearranges thermocouples onto the inner cylindrical surface, increasing thermocouple density per projected installation area while preserving leg length and electrical connectivity. By systematically examining printing conditions and electrode pattern design, we clarify how internal resistance and output characteristics are governed and demonstrate that the rolled geometry enhances footprint-normalized power generation by achieving a footprint-normalized power density of 18.9 nW cm −2 , which is 28.1 times higher than that of the Planar TEG. This work establishes autonomous paper self-folding as an effective design strategy for compact, fully printed thermoelectric devices.

Substrate-Induced One-Dimensional Borophene-Silver Hybridization

Modeling recreational visitation at Bureau of Land Management sites

Abstract Estimates of recreational visitation are essential for public land management. Visitation is typically estimated using devices such as automated counters that require logistics and effort, particularly at remote locations or those with several access points. In this study, we investigate the utility of alternative data sources and statistical models for estimating visitation to public lands in the United States, through an analysis of data from 70 Bureau of Land Management sites. We compile 1328 site-months of visitor count data collected on-site, which are used to train and evaluate three random forest models incorporating combinations of 15 site-level characteristics and three sources of digital mobility data—mobile device locations, geolocated social media, and community science observations. Models including site characteristics perform better than models relying on mobility data alone. Cross-validation using held-out sites reveal varying prediction accuracy, suggesting that model generalizability depends on the inclusion of characteristically similar sites in the training data. These results underscore the limitations of relying solely on mobility data for visitation estimation and highlight the benefits of combining diverse data sources. Our approach provides a scalable, data-driven framework for estimating visitation where traditional monitoring is challenging or infeasible, supporting broader applications in recreation management.

Adversarial vulnerability and robustness of deep learning models for panoramic dental X-ray segmentation

Room-Temperature Viscoelastic Liquid Semiconducting Block Copolymer with Mixed Ionic-Electronic Conduction

Formation of Quasi-2D [Mon+1Cn] Layered Structures via Carburizing Metal Films toward Surface Functional Group-Free Molybdenum Carbide MXene Thin Films

Abstract MXenes have a characteristic quasi-two-dimensional structure composed of covalently bonded transition metal (M) and carbon or nitrogen (X) sublayers, [Mn+1Xn], stacked with an interlayer spacing. Conventional MAX-etched MXenes inevitably have inhomogeneous surface functional groups (Tx), while chemical vapor deposition (CVD) has so far yielded only ultrathin α-Mo2C bulk crystals, failing to form a layered structure. Here, we report a CVD approach that enables the formation of layered [Mon+1Cn] structures through controlled carburization of predeposited Mo films without Tx sources. The resulting films are composed of multiple MXene phases, including Mo2C, Mo3C2, and Mo4C3 layered motifs. Regulated carbon diffusion results in discrete [Mon+1Cn] slabs with a well-defined interlayer spacing. In addition, a signature Tx-free, Mo-exposed surface exhibits enhanced nitrogen reduction reaction (NRR) activity compared with a CVD-grown α-Mo2C film, achieving an NH3 yield of 6.52 μg h–1 cm–2. Given the broad applicability to other MXenes, we anticipate that this work will stimulate further fundamental research and application on MXenes.