Tandem neural networks for designing nanoplasmonic structures to manipulate quantum dynamics.

In the cutting-edge field of quantum nanophotonics, embedding quantum emitters (QEs) into nanostructures goes beyond the conventional exploration of light-structure interactions. The ability to optimize the local density of states (LDOS) through the design of nanophotonic structures for quantum manipulation is a significant area of research. Traditional inverse design methods are inefficient when dealing with complex structures and multiparameter optimizations, and there are issues such as the design non-uniqueness of the designed structures.

DNMT3B aggravated renal fibrosis in diabetic kidney disease via activating Wnt/β-catenin signaling pathway.

The incidence of diabetic kidney disease (DKD) has increased rapidly worldwide in recent decades, and DKD is the leading cause of chronic kidney disease. The Wnt/β-catenin pathway is widely recognized as a critical contributor to DKD. However, how this pathway is activated in DKD is still unknown.

A deep learning system for detecting systemic lupus erythematosus from retinal images.

Systemic lupus erythematosus (SLE) is a serious autoimmune disorder predominantly affecting women. However, screening for SLE and related complications poses significant challenges globally, due to complex diagnostic criteria and public unawareness. Since SLE-related retinal involvement could provide insights into disease activity and severity, we develop a deep learning system (DeepSLE) to detect SLE and its retinal and kidney complications from retinal images.

A novel mesothelioma molecular classification based on malignant cell differentiation.

Background: The high heterogeneity and multi-directional poor differentiation of tumor cells in mesothelioma (MESO) contributes to tumor growth and malignant biological behaviors. However, a molecular classification based on differentiated states of tumor cells remains void.

Methods: We performed dimensionality reduction analysis on the single-cell RNA sequencing profiles available from the GEO database, to visualize the cell types in MESO.

An adaptive AI-based virtual reality sports system for adolescents with excess body weight: a randomized controlled trial.

Adolescents with obesity face numerous health risks and encounter barriers that lead to physical inactivity. We developed a virtual reality sports system, named REVERIE (Real-World Exercise and VR-Based Exercise Research in Education), which used deep reinforcement learning to train transformer-based virtual coaching agents, offering immersive and effective sports guidance, with biomechanical performance comparable to real-world physical sports. We integrated REVERIE into a randomized controlled trial involving an 8-week intervention in adolescents with excess body weight (n = 227).

A deep learning system for detecting silent brain infarction and predicting stroke risk.

Current brain imaging to detect silent brain infarctions (SBIs) is not feasible for the general population. Here, to overcome this challenge, we developed a retinal image-based deep learning system, DeepRETStroke, to detect SBI and refine stroke risk. We use 895,640 retinal photographs to pretrain the DeepRETStroke system, which encodes a domain-specific foundation model for representing eye-brain connections.

Quantum and Classical Exceptional Points at the Nanoscale: Properties and Applications.

Exceptional points (EPs) are the spectral singularities and one of the central concepts of non-Hermitian physics, originating from the inevitable energy exchange with the surrounding environment. EPs exist in diverse physical systems and give rise to many counterintuitive effects, offering rich opportunities to control the dynamics and alter the properties of optical, electronic, acoustic, and mechanical states. The last two decades have witnessed the flourishing of non-Hermitian physics and associated applications related to coalesced eigenstates at EPs in a plethora of classical systems.

Spatio-spectral localized modal coupling for room-temperature quantum coherence protection.

This work aims to advance the room-temperature manipulation of photonic qubits and enhance coherence preservation in and for quantum applications via tailored spatio-spectral localized (SSL) systems. We focus on an innovative all-plasmonic SSL system consisting of a gold bowtie array on a gold substrate. This design produces a high-Q spectral-localized mode through the lattice array, emerging from the collective lattice response of localized surface plasmon resonance (LSPR), particularly the surface lattice resonance (SLR).

Fibroblast Growth Factor 21 Levels Are Associated With Perception and Neural Responses to Sweetness in Type 2 Diabetes Mellitus.

Backgruound: The relationship between fibroblast growth factor 21 (FGF21) and sweet taste perception and preference in type 2 diabetes mellitus (T2DM) remains unclear. This study aims to investigate this relationship and examine the neural responses of T2DM patients to high-calorie sweet (HCS) food pictures, further exploring its correlation with FGF21 levels.

Methods: We assessed sweet taste perception and preference in 40 T2DM patients and 41 controls using classical scales.

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An Analysis of Temperature-Dependent Timing Jitter Factors in the Structural Design of Complementary Metal-Oxide-Semiconductor Single-Photon Avalanche Detectors.

Single-Photon Avalanche Photodiodes (SPADs) are increasingly utilized in high-temperature-operated, high-performance Light Detection and Ranging (LiDAR) systems as well as in ultra-low-temperature-operated quantum science applications due to their high photon sensitivity and timing resolution. Consequently, the jitter value of SPADs at different temperatures plays a crucial role in LiDAR systems and Quantum Key Distribution (QKD) applications. However, limited studies have been conducted on this topic.

Metabolic response to burn injury: a comprehensive bibliometric study.

Objective: Burns lead to systemic changes manifested by systemic disturbances in water-electrolyte balance and systemic metabolic and inflammatory responses. The hypermetabolic response after a burn injury relies on metabolic, hormonal, and inflammatory dysregulation mechanisms. This study aimed to provide a comprehensive bibliometric analysis of the burn metabolism research field, identifying key trends, influential contributors, and emerging research hotspots to inform future investigative efforts.

Insights into Metabolic Reprogramming in Tumor Evolution and Therapy.

Cancer remains a global health challenge, characterized not just by uncontrolled cell proliferation but also by the complex metabolic reprogramming that underlies its development and progression. This review delves into the intricate relationship between cancer and its metabolic alterations, drawing an innovative comparison with the cosmological concepts of dark matter and dark energy to highlight the pivotal yet often overlooked role of metabolic reprogramming in tumor evolution. It scrutinizes the Warburg effect and other metabolic adaptations, such as shifts in lipid synthesis, amino acid turnover, and mitochondrial function, driven by mutations in key regulatory genes.

[Synergistic removal of malachite green and Cr(Ⅵ) using ethylenediamine disuccinic acid functionalized silica gel].

As common industrial raw materials, malachite green (MG) and Cr(Ⅵ) generally coexist in waste liquids discharged from the paper printing, leather, and textile industries, causing serious harm to humans and the environment. Therefore, developing an effective method for the synergistic removal of MG and Cr(Ⅵ) from aquatic environments is of great research value. In this work, the non-homogeneous Fenton-like catalysts, namely, EDDS-Silica and EDDS-Co-Silica were successfully prepared using ethylenediamine disuccinic acid (EDDS) and silica gel (Silica) as raw materials, and a non-homogeneous Fenton-like catalytic method was developed for the efficient and synergistic removal of MG and Cr(Ⅵ) from wastewater.

Disease burden comparison and associated risk factors of early- and late-onset neonatal sepsis in China and the USA, 1990-2019.

Background: The morbidity and mortality rates of neonatal sepsis are high, with significant differences in risk factors and disease burden observed between developing and developed countries.

Objective: To provide evidence to support recommendations on improving public health policies using a comparative systematic analysis of the disease burden.

Methods: Using data from the Global Burden of Disease Study 2019, the prevalence and incidence of early- and late-onset neonatal sepsis and the disability-adjusted life years (DALYs) due to both countries in both China and the United States of America (USA) were assessed.

Comprehensive Review on Research Status and Progress in Precision Grinding and Machining of BK7 Glasses.

BK7 glass, with its outstanding mechanical strength and optical performance, plays a crucial role in many cutting-edge technological fields and has become an indispensable and important material. These fields have extremely high requirements for the surface quality of BK7 glass, and any small defects or losses may affect its optical performance and stability. However, as a hard and brittle material, the processing of BK7 glass is extremely challenging, requiring precise control of machining parameters to avoid material fracture or excessive defects.

Identification of differentially expressed tumour-related genes regulated by UHRF1-driven DNA methylation.

Ubiquitin-like with PHD and RING finger domains 1 (UHRF1) is an epigenetic regulator that plays critical roles in tumours. However, the DNA methylation alteration patterns driven by UHRF1 and the related differentially expressed tumour-related genes remain unclear. In this study, a UHRF1-shRNA MCF-7 cell line was constructed, and whole-genome bisulfite sequencing and RNA sequencing were performed.

The predictive value of TyG and lipid ratios on the development of complications and hyperuricemia in patients with type 2 diabetes mellitus.

Investigate the predictive value of TyG and lipid ratios on the development of complications and HUA in patients with T2DM. A retrospective cross-sectional study involving 9488 T2DM patients was conducted. They were divided into HUA and NUA group base on SUA level and divided into with and without complications groups according to the diagnosis of the endocrinologist.

A 10-year mono-center study on patients with burns ≥70% TBSA: prediction model construction and multicenter validation - retrospective cohort.

Background: Burn injuries with ≥70% total body surface area (TBSA) are especially acute and life-threatening, leading to severe complications and terrible prognosis, while a powerful model for the prediction of overall survival (OS) is lacking. The objective of this study is to identify prognostic factors for the OS of patients with burn injury ≥70% TBSA and construct and validate a feasible predictive model.

Materials And Methods: Patients diagnosed with burns ≥70% TBSA admitted and treated between 2010 and 2020 in our hospital were included.

Characterization of Urinary -Acetyltaurine as a Biomarker of Hyperacetatemia in Mice.

Acetate is an important metabolite in metabolic fluxes. Its presence in biological entities originates from both exogenous inputs and endogenous metabolism. Because the change in blood acetate level has been associated with both beneficial and adverse health outcomes, blood acetate analysis has been used to monitor the systemic status of acetate turnover.

Deterministic positioning and alignment of a single-molecule exciton in plasmonic nanodimer for strong coupling.

Experimental realization of strong coupling between a single exciton and plasmons remains challenging as it requires deterministic positioning of the single exciton and alignment of its dipole moment with the plasmonic fields. This study aims to combine the host-guest chemistry approach with the cucurbit[7]uril-mediated active self-assembly to precisely integrate a single methylene blue molecule in an Au nanodimer at the deterministic position (gap center of the nanodimer) with the maximum electric field (EF) and perfectly align its transition dipole moment with the EF, yielding a large spectral Rabi splitting of 116 meV for a single-molecule exciton-matching the analytical model and numerical simulations. Statistical analysis of vibrational spectroscopy and dark-field scattering spectra confirm the realization of the single exciton strong coupling at room temperature.

MKG-GC: A multi-task learning-based knowledge graph construction framework with personalized application to gastric cancer.

Over the past decade, information for precision disease medicine has accumulated in the form of textual data. To effectively utilize this expanding medical text, we proposed a multi-task learning-based framework based on hard parameter sharing for knowledge graph construction (MKG), and then used it to automatically extract gastric cancer (GC)-related biomedical knowledge from the literature and identify GC drug candidates. In MKG, we designed three separate modules, MT-BGIPN, MT-SGTF and MT-ScBERT, for entity recognition, entity normalization, and relation classification, respectively.