Validity of Digital Photographic Images for Dietary Assessment of Participants with Low Frequency of Home-Made Meal Intake.

It is important to clarify the factors that cause errors when calculating nutrient and food group intake using dietary surveys. This study aimed to clarify the characteristics of errors in nutrient and food group intake among those who ate out and those who consumed ready-made meals when they recorded their meals using photographic and weighing methods. In October 2020, 38 second- and third-year students who were enrolled in dietitian training programs at two Japanese universities were asked to record their meals using both photographic and weighed records.

[Research progress on the early warning of heart failure based on remote dynamic monitoring technology].

Heart failure (HF) is the end-stage of all cardiac diseases, characterized by high prevalence, high mortality, and heavy social and economic burden. Early warning of HF exacerbation is of great value for outpatient management and reducing readmission rates. Currently, remote dynamic monitoring technology, which captures changes in hemodynamic and physiological parameters of HF patients, has become the primary method for early warning and is a hot research topic in clinical studies.

[Advances in multimodal biomedical imaging of small animals].

Small animal multimodal biomedical imaging refers to the integration of multiple imaging techniques within the same system or device to acquire comprehensive physiological and pathological information of small animals, such as mice and rats. With the continuous advancement of biomedical research, this cutting-edge technology has attracted extensive attention. Multimodal imaging techniques, based on diverse imaging principles, overcome the limitations of single-modal imaging through information fusion, significantly enhancing the overall system's sensitivity, temporal/spatial resolution, and quantitative accuracy.

[Research progress in electroencephalogram-based brain age prediction].

Brain age prediction, as a significant approach for assessing brain health and early diagnosing neurodegenerative diseases, has garnered widespread attention in recent years. Electroencephalogram (EEG), an non-invasive, convenient, and cost-effective neurophysiological signal, offers unique advantages for brain age prediction due to its high temporal resolution and strong correlation with brain functional states. Despite substantial progress in enhancing prediction accuracy and generalizability, challenges remain in data quality and model interpretability.

[Brain midline segmentation method based on prior knowledge and path optimization].

To address the challenges faced by current brain midline segmentation techniques, such as insufficient accuracy and poor segmentation continuity, this paper proposes a deep learning network model based on a two-stage framework. On the first stage of the model, prior knowledge of the feature consistency of adjacent brain midline slices under normal and pathological conditions is utilized. Associated midline slices are selected through slice similarity analysis, and a novel feature weighting strategy is adopted to collaboratively fuse the overall change characteristics and spatial information of these associated slices, thereby enhancing the feature representation of the brain midline in the intracranial region.

[Evaluation method and system for aging effects of autonomic nervous system based on cross-wavelet transform cardiopulmonary coupling].

Heart rate variability time and frequency indices are widely used in functional assessment for autonomic nervous system (ANS). However, this method merely analyzes the effect of cardiac dynamics, overlooking the effect of cardio-pulmonary interplays. Given this, the present study proposes a novel cardiopulmonary coupling (CPC) algorithm based on cross-wavelet transform to quantify cardio-pulmonary interactions, and establish an assessment system for ANS aging effects using wearable electrocardiogram (ECG) and respiratory monitoring devices.

[Motor imagery classification based on dynamic multi-scale convolution and multi-head temporal attention].

Convolutional neural networks (CNNs) are renowned for their excellent representation learning capabilities and have become a mainstream model for motor imagery based electroencephalogram (MI-EEG) signal classification. However, MI-EEG exhibits strong inter-individual variability, which may lead to a decline in classification performance. To address this issue, this paper proposes a classification model based on dynamic multi-scale CNN and multi-head temporal attention (DMSCMHTA).

[Technical maturity and bubble risks of brain-computer interface (BCI): Considerations from research to industrial translation].

Brain-computer interface (BCI) technology faces structural risks due to a misalignment between its technological maturity and industrialization expectations. This study used the Technology Readiness Level (TRL) framework to assess the status of major BCI paradigms-such as steady-state visual evoked potential (SSVEP), motor imagery, and P300-and found that they predominantly remained at TRL4 to TRL6, with few stable applications reaching TRL9. The analysis identified four interrelated sources of bubble risk: overly broad definitions of BCI, excessive focus on decoding performance, asynchronous translational progress, and imprecise terminology usage.

An MRI-based model for preoperative prediction of tertiary lymphoid structures in patients with gallbladder cancer.

Objectives: To predict tertiary lymphoid structures (TLSs) in gallbladder cancer (GBC) using preoperative magnetic resonance imaging (MRI)-based radiomics.

Methods: Patients with GBC from two centres served as training (n = 129) and external validation (n = 44) cohorts. Radiomics features were extracted from six imaging sequences for inclusion in a radiomics model (Rad-score).

Association of plasma Alzheimer's disease biomarkers with cognitive decline in cognitively unimpaired individuals.

Introduction: Plasma biomarkers' utility for predicting incident mild cognitive impairment (MCI) remains unclear. We evaluated associations of plasma Alzheimer's disease (AD) biomarkers and amyloid positron emission tomography (PET) with transitions from cognitively unimpaired (CU) to MCI in the Mayo Clinic Study of Aging (MCSA) and BioFINDER-2 studies.

Methods: Associations of continuous baseline plasma biomarker levels and amyloid PET Centiloid with progression to MCI, adjusting for age, sex, and education, were evaluated with Cox proportional hazards models.

Contact-dominated localized electric-displacement-field-enhanced pressure sensing.

Pressure sensors, especially the typical capacitive sensors that feature low power consumption, have drawn considerable interest in emerging and rapidly growing fields such as flexible electronics and humanoid robots, but often suffer from limited performance. Here, we report a contact-dominated design for capacitive pressure sensors to dramatically improve the sensing response and linearity over a broad pressure range. This design is implemented by utilizing hierarchical microstructured electrodes made of robust conductive composites with metallic coverage and layered dielectrics with high unit-area capacitance to realize localized electric-displacement-field-enhanced capacitance change.

Exploring the impact of note taking methods on cognitive function among university students.

Background: Taking notes during lectures plays a vital role in enhancing learning outcomes. With technological advancements, digital note-taking has gained popularity among university students in recent years due to its convenience, ease of storage, sharing, and searching. Different versions of digital note-taking have been introduced, including the use of styluses on tablets, which offer a blend of traditional handwriting and digital advantages.

Alzheimer's disease traits in Parkinson's disease without α-synuclein seeding.

Introduction: The α-synuclein (αSyn) seed amplification assay (αSyn-SAA) is an accurate tool to detect αSyn seeds in patients with Parkinson's disease (PD). However, a minority of clinically diagnosed PD patients are negative for αSyn.

Methods: The αSyn-SAA was performed in cerebrospinal fluid (CSF) of individuals with PD (n = 93), multiple system atrophy (MSA, n = 26), progressive supranuclear palsy (PSP, n = 18), corticobasal syndrome (n = 3), and healthy controls (n = 29).

Diurnal variation mapping of urban NO concentrations at high spatial resolution using mobile phone signaling data.

Atmospheric NO remains one of the primary pollutant gases in urban areas. However, current techniques such as ground-based measurements, remote sensing, and atmospheric chemistry models have persistently faced challenges in monitoring sub-daily NO concentrations at high spatial resolution. Mobile phone signaling data holds significant potential for characterizing human activity intensity, offering new opportunities to track near-surface NO concentrations with refined spatiotemporal precision.

Defect-engineered amorphous-like nanointerceptors for T MRI-Guided treatment of reperfusion injury.

Ischemic reperfusion (I/R) injury is dominated by excessive reactive oxygen species (ROS)-mediated oxidative damage and uncontrolled inflammation, yet effective strategies for simultaneous diagnosis and treatment remain elusive. Herein, we report a defect-engineered amorphous-like MnCeO nanointerceptor with dual capabilities of magnetic resonance imaging (MRI) -guided stroke diagnosis and ROS-scavenging therapy. The synergistic effect of the amorphous-like structure and Mn-Ce solid solution induces abundant oxygen vacancies and a disordered surface, significantly boosting ROS catalytic removal.

Sequential interfacial contributions of microplastics to microbial adhesion and metal adsorption.

Microplastics (MPs) are increasingly recognized as interfacial substrates for microbial adhesion and metal adsorption in aquatic environments. However, the temporal sequence and causality of MPs-microbial-metal interactions remain poorly understood. This study uncovers the mechanistic sequence and interfacial contributions of MPs in mediating B.

An artificial intelligence cloud platform for OCT-based retinal anomalies screening system in real clinical environments.

Millions of individuals worldwide suffer from retinal anomalies, which can lead to irreversible vision loss. However, the number of ophthalmologists is highly mismatched with the population base in China, especially in many rural and underdeveloped towns. To tackle these challenges, this paper developed an Artificial Intelligence Cloud Platform for OCT-based Retinal Anomalies Screening (AI-PORAS), which is capable of detecting 15 retinal anomalies in OCT images to enhance remote diagnostic efficiency.

High-fidelity geometric quantum gates exceeding 99.9% in germanium quantum dots.

Achieving high-fidelity and robust qubit manipulations is a crucial requirement for realizing fault-tolerant quantum computation. Here, we demonstrate a single-hole spin qubit in a germanium quantum dot and characterize its control fidelity using gate set tomography. The maximum control fidelities reach 97.

Subsecond optically controlled domain switching in freestanding ferroelectric BaTiO membrane.

The quest to develop energy-efficient and fast optoelectronic control of memory devices is essential. In this respect, ferroelectric materials are gaining tremendous importance in information and communication technology. Here, we demonstrate light-controlled polarisation switching on a subsecond timescale ( <500 ms) in a freestanding BaTiO membrane, which is nearly 1200 times faster than the previously reported response using a BaTiO thin film.

Ligand-Engineered All-In-One Cu(I) Iodide Complex Enables Near-Unity Photoluminescence and Advanced 3D X-ray Image Reconstruction.

High-performance X-ray detectors are essential for 3D X-ray imaging in computed tomography (CT), but conventional systems require high radiation doses to achieve fine resolution. All-In-One (AIO) Cu(I) halide complexes, capable of forming both ionic and coordinate bonds within a single structure, offer efficient scintillation at lower doses, yet their performance remains limited by nonradiative energy losses during indirect X-ray-to-light conversion. Here, we develop rigid-cation-assisted AIO Cu(I) halide complexes by introducing π-π interactions to suppress the nonradiative pathways, achieving near-unity photoluminescence quantum yield (PLQY).

Siloxane-Tethered Halogenated Thiophene Additives Enable Excellent Fibrillar Morphology and Air Processing for High-Performing Polymer Solar Cells.

For organic solar cells (OSCs), morphological optimization and high-performance air-processing of the photoactive layer are important concerns. Herein, three siloxane-tethered halogenated thiophene additives Cl-Th-SiO, Br-Th-SiO, and I-Th-SiO, with chlorine, bromine, and iodine substitutions on the thiophene, are designed and synthesized, respectively. With the three additives, fibrillar active layer networks based on polymer donor D18 and nonfullerene acceptor L8-BO can be achieved.

Neutrophil gelatinase-associated lipocalin and fibrinogen-to-albumin ratio are indicators of kidney disease in sickle cell disease patients with microalbuminuria: a multicentre case-control study in Ghana.

Background: Neutrophil gelatinase-associated lipocalin (NGAL) is present in secondary granules of neutrophils and it is a relatively newly recognized marker of kidney diseases. The fibrinogen-to-albumin ratio (FAR) is a marker of inflammation but its diagnostic value has not been determined in sickle cell disease patients with kidney diseases. This study investigated the diagnostic roles of serum neutrophil gelatinase-associated lipocalin (sNGAL) and FAR for kidney diseases in steady-state adult sickle cell disease (SCD) patients.

Prediction of cancer drug response based on heterogeneous graph neural networks and multi-omics data.

Precision prediction of cancer drug response remains a critical challenge in personalized medicine. With ongoing advancements in related research, substantial amounts of cell line omics data and drug feature information have been accumulated, offering valuable resources for investigating cancer drug responses. However, effectively integrating these multi-omics features and constructing accurate and interpretable network-based prediction models remain challenging.

Enriched environment inhibits GLT-1 ubiquitination by downregulating SMURF1 to attenuate ischemic brain injury induced excitotoxicity.

Background: Excitotoxicity induced by glutamate contributes significantly to ischemic brain injury. The role of enriched environment (EE) in promoting neurological recovery post-stroke is well-established, yet its impact on excitotoxicity remains unclear. This study aimed to elucidate the mechanisms through which EE modulates excitotoxicity in ischemic brain injury using in vivo and in vitro experiments.

Novel surveillance protocol for gastric cancer based on CEA: a high-volume multi-center study.

Tumor markers are commonly used in gastric cancer patients, but their effectiveness in monitoring recurrences is not optimal. This study aims to evaluate the recurrence predictive performance of carcinoembryonic antigen (CEA) across different baseline groups and establish a new surveillance protocol. We analyzed data from 1708 patients at Sun Yat-Sen University Cancer Center with stage I-III gastric adenocarcinoma.