Neuroimaging biomarkers distinguishing medication-naïve adolescents with major depressive disorder and bipolar disorder during depressive episodes.

Background: The adolescent period represents a critical window of heightened vulnerability for the onset of major depressive disorder (MDD) and bipolar disorder (BD). Nevertheless, the neuro-metabolic abnormalities in adolescents with MDD and BD remain incompletely understood, and the early differentiation between MDD and BD in depressive episodes continues to pose significant clinical challenges.

Method: This study involved the recruitment of 60 adolescent MDD, 50 adolescent BD patients in depressed episodes, and 48 demographically matched healthy controls (HCs).

Polyethylene microplastics and nanoplastics colored with inorganic pigments in aquatic environments: Effects of mechanical aging on physicochemical properties, aggregation kinetics, and metal release.

Mechanical aging affects the environmental fate and transport of colored plastics. This study investigated the physicochemical properties, aggregation kinetics, and metal release of polyethylene (PE) microplastics (PEMPs) and nanoplastics (PENPs) aged from masterbatches. Colored PE with inorganic pigments, including White-Ti-PE (titanium dioxide), Red-Fe-PE (iron oxide), Yellow-Cr/Pb-PE (lead chromate), and Blue-Al-PE (ultramarine), was compared with Transparent-PE.

Aggregation and deposition kinetics of polystyrene nanoplastics in lung fluids: Influence of particle property, fluid condition, and surfactant protein.

Nanoplastics (NPs) are emerging atmospheric contaminants that aggregate and deposit in lung fluids post-inhalation, affecting their migration and health risks. This study investigated the aggregation and deposition kinetics of six polystyrene NPs (PSNPs): NP50, NP100, NP500, A-NP50 and A-NP100 (amino-modified), and C-NP100 (carboxyl-modified), in artificial lysosomal fluid (ALF) and Gamble's solution (GMB). In ALF, PSNPs aggregated within 20 min to 132-1066 nm, with rates ranking A-NP50 > NP100 > A-NP100 > C-NP100 > NP50 > NP500.

Aggregation kinetics of diesel soot nanoparticles in lung fluids: Effects of exposure conditions, fluid properties, and pulmonary surfactant lipids.

Soot nanoparticles (SNPs) are carbonaceous particulate matter with significant environmental and health impacts. Once inhaled, their aggregation in the respiratory system can influence their migration patterns and health hazards. This study investigated the effects of exposure conditions (interaction time, particle concentration, and activity state), fluid properties (pH and composition), and pulmonary surfactant lipids [micro-sized (m-DPPC) and nano-sized dipalmitoylphosphatidylcholine (n-DPPC)] on aggregation kinetics of SNPs in five lung fluids.

The abnormal choline to creatine ratio of the right anterior cingulate gyrus is linked to cognitive impairment in youth with major depressive disorder.

Background: Previous studies indicated that the notion that 20-40 % of patients with major depressive disorder (MDD) have cognitive impairments (CI). The mechanism of cognitive deficits in MDD is largely unknown. Recent evidence suggests that metabolic changes may be associated with poorer cognitive outcomes in MDD.

DECA: harnessing interpretable transformer model for cellular deconvolution of chromatin accessibility profile.

The assay for transposase-accessible chromatin with sequencing (ATAC-seq) identifies chromatin accessibility across the genome, crucial for gene expression regulating. However, bulk ATAC-seq obscures cellular heterogeneity, while single-cell ATAC-seq suffers from issues such as sparsity and costliness. To this end, we introduce DECA, a sophisticated deep learning model based on vision transformer to deconvolve cell type information from bulk chromatin accessibility profiles, utilizing single-cell ATAC-seq datasets as reference for enhanced precision and resolution.

Multi-Omics analysis reveals the sensory quality and fungal communities of Tibetan teas produced by wet- and dry-piling fermentation.

Ya'an Tibetan tea, a dark tea with a rich historical heritage, is typically processed using two primary piling fermentation methods: wet piling with rolled leaves (moisture content around 60%) and dry piling with sun-dried or baked green tea leaves (moisture content below 30%). This study employed sensory evaluation, targeted and non-targeted metabolomics, and fungal Internal Transcribed Spacer (ITS) sequencing to investigate changes in quality components and fungal composition in Tibetan tea processed by both wet and dry-piling methods. The results revealed that 3,7-Dimethyl-1,5,7-octatriene-3-ol and D-limonene were identified as key volatile metabolites contributing to the aroma variations between the dry and wet-piled teas.

Evolution of the biochemistry underpinning purine alkaloid metabolism in plants.

Purine alkaloids are naturally occurring nitrogenous methylated derivatives of purine nucleotide degradation products, having essential roles in medicine, food and various other aspects of our daily lives. They are generated through convergent evolution in different plant species. The pivotal reaction steps within the purine alkaloid metabolic pathways have been largely elucidated, and the convergent evolution of purine alkaloids has been substantiated through bioinformatic, biochemical and other research perspectives within -adenosyl-ʟ-methionine-dependent -methyltransferases.

Heteroaggregation, disaggregation, and migration of nanoplastics with nanosized activated carbon in aquatic environments: Effects of particle property, water chemistry, and hydrodynamic condition.

Nanosized activated carbon (NAC) as emerging engineered nanomaterials may interact with nanoplastics prevalent in aquatic environments to affect their fate and transport. This study investigated the effects of particle property (charge and concentration), water chemistry [electrolytes, pH, humic acid (HA), and sodium alginate (SA)], and hydrodynamic condition [wave (i.e.

Influence of Seamless Patient-Centered Care on Efficiency, Satisfaction, and Patient Awareness in Imaging Departments: A Prospective Cohort Study.

Aims: This study aimed to enhance the existing nursing model in imaging departments by implementing a characteristic seamless nursing care approach and assessing its impact on patient and medical staff satisfaction, nursing quality, examination efficiency, and patient awareness. We hypothesized that the implementation of a seamless nursing care model would be associated with higher patient satisfaction, improved nursing quality, increased examination efficiency, and better patient awareness compared to the traditional nursing model.

Materials And Methods: This prospective cohort study included 300 patients undergoing imaging examinations from January 2019 to January 2022.

Tracing unknown tumor origins with a biological-pathway-based transformer model.

Cancer of unknown primary (CUP) represents metastatic cancer where the primary site remains unidentified despite standard diagnostic procedures. To determine the tumor origin in such cases, we developed BPformer, a deep learning method integrating the transformer model with prior knowledge of biological pathways. Trained on transcriptomes from 10,410 primary tumors across 32 cancer types, BPformer achieved remarkable accuracy rates of 94%, 92%, and 89% in primary tumors and primary and metastatic sites of metastatic tumors, respectively, surpassing existing methods.

SIMarker: Cellular similarity detection and its application to diagnosis and prognosis of liver cancer.

Background: The emergence of single-cell technology offers a unique opportunity to explore cellular similarity and heterogeneity between precancerous diseases and solid tumors. However, there is lacking a systematic study for identifying and characterizing similarities at single-cell resolution.

Methods: We developed SIMarker, a computational framework to detect cellular similarities between precancerous diseases and solid tumors based on gene expression at single-cell resolution.

A Novel Deep Convolutional Neural Network Combining Global Feature Extraction and Detailed Feature Extraction for Bearing Compound Fault Diagnosis.

This study researched the application of a convolutional neural network (CNN) to a bearing compound fault diagnosis. The proposed idea lies in the ability of CNN to automatically extract fault features from complex raw signals. In our approach, to extract more effective features from a raw signal, a novel deep convolutional neural network combining global feature extraction with detailed feature extraction (GDDCNN) is proposed.

Predicting heart failure in-hospital mortality by integrating longitudinal and category data in electronic health records.

Heart failure is a life-threatening syndrome that is diagnosed in 3.6 million people worldwide each year. We propose a deep fusion learning model (DFL-IMP) that uses time series and category data from electronic health records to predict in-hospital mortality in patients with heart failure.

Prioritizing prognostic-associated subpopulations and individualized recurrence risk signatures from single-cell transcriptomes of colorectal cancer.

Colorectal cancer (CRC) is one of the most common gastrointestinal malignancies. There are few recurrence risk signatures for CRC patients. Single-cell RNA-sequencing (scRNA-seq) provides a high-resolution platform for prognostic signature detection.

A review of deep learning-based multiple-lesion recognition from medical images: classification, detection and segmentation.

Deep learning-based methods have become the dominant methodology in medical image processing with the advancement of deep learning in natural image classification, detection, and segmentation. Deep learning-based approaches have proven to be quite effective in single lesion recognition and segmentation. Multiple-lesion recognition is more difficult than single-lesion recognition due to the little variation between lesions or the too wide range of lesions involved.

CBA-UNet: A context-coordination multi-atlas boundary-aware UNet-like method for PET/CT images based tumor segmentation.

Tumor segmentation is a necessary step in clinical processing that can help doctors diagnose tumors and plan surgical treatments. Since tumors are usually small, the locations and appearances vary substantially across individuals, and the contrast between tumors and adjacent normal tissues is low, tumor segmentation is still a challenging task. Although convolutional neural networks (CNNs) have achieved good results in tumor segmentation, the information about tumor boundaries has been rarely explored.

Factors Influencing Users' Perceived Restoration While Using Treetop Trails: The Case of the Fu and Jinjishan Forest Trails, Fuzhou, China.

Studies have indicated that urban greenways promote physical and perceived restoration. However, there is a lack of research on the impact of treetop trails on human perceived restoration. In this study, two representative treetop trails in Fuzhou city were selected to investigate treetop trails' impact on users' perceived restoration.

Colloidal stability of nanosized activated carbon in aquatic systems: Effects of pH, electrolytes, and macromolecules.

Nanosized activated carbon (NAC) is a novel adsorbent with great potential for water reclamation. However, its transport and reactivity in aqueous environments may be greatly affected by its stability against aggregation. This study investigated the colloidal stability of NAC in model aqueous systems with broad background solution chemistries including 7 electrolytes (NaCl, NaNO, NaSO, KCl, CaCl, MgCl, and BaCl), pH 4-9, and 6 macromolecules (humic acid (HA), fulvic acid (FA), cellulose (CEL), bovine serum albumin (BSA), alginate (ALG), and extracellular polymeric substance (EPS)), along with natural water samples collected from pristine to polluted rivers.