Novel cuprotosis-related gene signature: a prognostic indicator and regulator of the glioma immune microenvironment.

Background: Glioma is a primary malignant brain tumor with a poor prognosis. Glioma-related biomarkers need to be identified to enable the personalized treatment of and predict the prognosis of glioma patients. Cuproptosis is an unusual mechanism of cell death, and is closely associated with disease progression and the immune-microenvironment of the tumor.

Enhancing Chordoma Radiotherapy: Ta@PVP Nanoparticles as Potent Radiosensitizers.

Surgical resection and high-dose radiotherapy constitute the standard therapeutic approaches for chordoma. However, the efficacy of radiotherapy is often compromised by the tumor microenvironment's hypoxic conditions, which confer radiation resistance, and by the potential damage to adjacent spinal cord and neural structures from elevated radiation doses. To address these challenges, we employed high biocompatible poly(vinylpyrrolidone)-modified tantalum nanoparticles (Ta@PVP NPs) as a potent radiosensitizer to augment the radiotherapy sensitivity of chordoma.

Improved bioavailability of polydatin and its protective effect against cisplatin induced nephrotoxicity through self-assembled fucoidan and carboxymethyl chitosan delivery system.

Cisplatin induced acute kidney injury (AKI) is clinically prevalent, with a complex pathogenesis and a lack of effective therapeutic drugs. Polydatin (Po) has excellent biological activity, but its low solubility and bioavailability limit its application. In this study, fucoidan (Fu) and carboxymethyl chitosan (Cs) self-assembled into nanoparticles through electrostatic interactions/hydrogen bonding and loaded Po (Fu/Cs Po NPs).

Deep Learning-Assisted Design of Novel Donor-Acceptor Combinations for Organic Photovoltaic Materials with Enhanced Efficiency.

Designing donor (D) and acceptor (A) structures and discovering promising D-A combinations can effectively improve organic photovoltaic (OPV) device performance. However, to obtain excellent power conversion efficiency (PCE), the trial-and-error structural design in the infinite chemical space is time-consuming and costly. Herein, a deep learning (DL)-assisted design framework for OPV materials is proposed.

Depth-resolved imaging through scattering media using time-gated light field tomography.

We present a novel, to the best of our knowledge, approach to overcome the limitations imposed by scattering media using time-gated light field tomography. By integrating the time-gating technique with light field imaging, we demonstrate the ability to capture and reconstruct images with different depths through highly scattering environments. Our method exploits the temporal characteristics of light propagation to selectively isolate ballistic photons, enabling enhanced depth resolution and improved imaging quality.

Cell Communications Between GCs and Macrophages Contribute to the Residence of Macrophage in Preovulatory Follicles.

Problem: There were not only granulosa cells (GCs) but also immune cells in preovulatory follicular fluid. The objective of this study was to explore the interactions between macrophages and GCs via adhesion molecules in preovulatory follicles and the regulatory mechanisms of the interactions.

Method Of Study: Flow cytometry and immunofluorescence were used to detect the expression of ITGB1 in macrophages and fibronectin (FN)1 in GCs in preovulatory follicles from 12 patients.

Frontiers in artificial intelligence-directed light-sheet microscopy for uncovering biological phenomena and multi-organ imaging.

Light-sheet fluorescence microscopy (LSFM) introduces fast scanning of biological phenomena with deep photon penetration and minimal phototoxicity. This advancement represents a significant shift in 3-D imaging of large-scale biological tissues and 4-D (space + time) imaging of small live animals. The large data associated with LSFM requires efficient imaging acquisition and analysis with the use of artificial intelligence (AI)/machine learning (ML) algorithms.

Quantitative analysis of imaging characteristics in lung adenocarcinoma in situ using artificial intelligence.

Background: With the rising incidence of pulmonary nodules (PNs), lung adenocarcinoma in situ (AIS) is a critical early stage of lung cancer, necessitating accurate diagnosis for early intervention. This study applies artificial intelligence (AI) for quantitative imaging analysis to differentiate AIS from atypical adenomatous hyperplasia (AAH) and minimally invasive adenocarcinoma (MIA), aiming to enhance clinical diagnosis and prevent misdiagnosis.

Methods: The study analyzed 1215 PNs with confirmed AAH, AIS, and MIA from six centers using the Shukun AI diagnostic module.

Advances in Miniaturized Computational Spectrometers.

Miniaturized computational spectrometers have emerged as a promising strategy for miniaturized spectrometers, which breaks the compromise between footprint and performance in traditional miniaturized spectrometers by introducing computational resources. They have attracted widespread attention and a variety of materials, optical structures, and photodetectors are adopted to fabricate computational spectrometers with the cooperation of reconstruction algorithms. Here, a comprehensive review of miniaturized computational spectrometers, focusing on two crucial components: spectral encoding and reconstruction algorithms are provided.

Advancements in enzymatic reaction-mediated microbial transformation.

Enzymatic reaction-mediated microbial transformation has emerged as a promising technology with significant potential in various industries. These technologies offer the ability to produce enzymes on a large scale, optimize their functionality, and enable sustainable production processes. By utilizing microbial hosts and manipulating their genetic makeup, enzymes can be synthesized efficiently and tailored to meet specific industrial requirements.

Reducing Dielectric Loss of High-Dielectric-Constant Elastomer via Rigid Short-Chain Crosslinking.

High-dielectric-constant elastomers have broad applications in wearable electronics, which can be achieved by the elastification of relaxor ferroelectric polymers. However, the introduction of soft long chains, with their high mobility under strong electric fields, leads to high dielectric loss. Given the relatively low modulus of relaxor ferroelectric polymers, elastification can be realized by introducing short-chain crosslinkers.

Hyperspectral retinal imaging in Alzheimer's disease and age-related macular degeneration: a review.

While Alzheimer's disease and other neurodegenerative diseases have traditionally been viewed as brain disorders, there is growing evidence indicating their manifestation in the eyes as well. The retina, being a developmental extension of the brain, represents the only part of the central nervous system that can be noninvasively imaged at a high spatial resolution. The discovery of the specific pathological hallmarks of Alzheimer's disease in the retina of patients holds great promise for disease diagnosis and monitoring, particularly in the early stages where disease progression can potentially be slowed.

Highly elastic relaxor ferroelectrics for wearable energy storage.

Polymer-based relaxor ferroelectrics with high dielectric constant are pivotal in cutting-edge electronic devices, power systems, and miniaturized pulsed electronics. The surge in flexible electronics technology has intensified the demand for elastic ferroelectric materials that exhibit excellent electrical properties and mechanical resilience, particularly for wearable devices and flexible displays. However, as an indispensable component, intrinsic elastomers featuring high dielectric constant and outstanding resilience specifically tailored for elastic energy storage remain undeveloped.

Induction of Non-Canonical Ferroptosis by Targeting Clusters Suppresses Glioblastoma.

Glioblastoma multiforme (GBM) is the most aggressive brain tumor. There is a pressing need to develop novel treatment strategies due to the poor targeting effect of current therapeutics. Here, a gold cluster coated with optimized GBM-targeting peptide is engineered, namely NA.

Light-field tomographic fluorescence lifetime imaging microscopy.

Fluorescence lifetime imaging microscopy (FLIM) is a powerful imaging technique that enables the visualization of biological samples at the molecular level by measuring the fluorescence decay rate of fluorescent probes. This provides critical information about molecular interactions, environmental changes, and localization within biological systems. However, creating high-resolution lifetime maps using conventional FLIM systems can be challenging, as it often requires extensive scanning that can significantly lengthen acquisition times.

Highly elastic relaxor ferroelectric peroxide crosslinking.

Relaxor ferroelectrics are well-known for their high dielectric constants, low dielectric losses, and excellent electromechanical properties, making them valuable for various electronic devices. Despite recent efforts to enhance the durability of ferroelectrics through chemical cross-linking, achieving elasticity in relaxor ferroelectric materials remains a significant challenge. These materials inherently possess traits such as low crystallinity and small crystal size, while chemical crosslinking tends to diminish polymer crystallinity considerably.

Magnetostrictive bi-perceptive flexible sensor for tracking bend and position of human and robot hand.

The sensor that simultaneously perceives bending strain and magnetic field has the potential to detect the finger bending state and hand position of the human and robot. Based on unique magneto-mechanical coupling effect of magnetostrictive materials, the proposed a bi-perceptive flexible sensor, consisting of the Co-Fe film and magnetic sensing plane coils, can realize dual information perception of strain/magnetic field through the change of magnetization state. The sensor structure and interface circuit of the sensing system are designed to provide high sensitivity and fast response, based on the input-output characteristics of the simulation model.