OmniCellAgent: Towards AI Co-Scientists for Scientific Discovery in Precision Medicine.

The convergence of large language models (LLMs), AIagents, and large-scale omic datasets-such as single-cell omics, marks the arrival of a critical inflection point in biomedical research, via autonomous data mining and novel hypothesis generation. However, there is no specifically designed agentic AI model that can systematically integrate large-scale single-cell (sc) RNAseq (covering diverse diseases and cell types), omic data analytic tools, accumulated biomedical knowledge, and literature search to facilitate autonomous scientific discovery in precision medicine. In this study, we develop a novel agentic AI, OmniCellAgent, to empower non-computational-expert users-such as patients and family members, clinicians, and wet-lab researchers-to conduct scRNA-seq data-driven biomedical research like experts, uncovering molecular disease mechanisms and identifying effective precision therapies.

The effect of periodontal status in the associations between socioeconomic status and cognitive performance: a mediation analysis in older adults.

Introduction: The aim of this study is to analyse the association of socioeconomic status (SES) with cognitive performance, and the mediation effect of periodontal status in this relationship in the National Health and Nutrition Examination Survey (NHANES) database from 2011-2014.

Methods: The SES was evaluated based on poverty-income ratio (PIR), occupation, educational level, and health insurance using latent class analysis. Multivariable logistic regressions were used to determine the association of cognitive performance, examined by Consortium to Establish a Registry for Alzheimer's Disease (CERAD) test, animal fluency test (AFT), and digit symbol substitution test (DSST), with SES, attachment loss (AL) and probing depth (PD).

Oxidative balance score and mortality: mediating role of insulin resistance across age strata in the NHANES cohort.

Background: This study examined the association between oxidative balance score (OBS), a composite measure of oxidative/antioxidative factors, and mortality, while investigating insulin resistance (IR) indices as potential mediators using a nationally representative cohort.

Methods: A cohort of 11,849 U. S.

M3NetFlow: A multi-scale multi-hop graph AI model for integrative multi-omic data analysis.

Multi-omic data-driven studies are at the forefront of precision medicine by characterizing complex disease signaling systems across multiple views and levels. The integration and interpretation of multi-omic data are critical for identifying disease targets and deciphering disease signaling pathways. However, it remains an open problem due to the complex signaling interactions among many proteins.

Frontier molecular orbital weighted model based networks for revealing organic delayed fluorescence efficiency.

Free of noble-metal and high in unit internal quantum efficiency of electroluminescence, organic molecules with thermally activated delayed fluorescence (TADF) features pose the potential to substitute metal-based phosphorescence materials and serve as the new-generation emitters for the mass production of organic light emitting diodes (OLEDs) display. Predicting the function of TADF emitters beyond classic chemical synthesis and material characterization experiments remains a great challenge. The advances in deep learning (DL) based artificial intelligence (AI) offer an exciting opportunity for screening high-performance TADF materials through efficiency evaluation.

Recent advances in nanotherapy-based treatment of epilepsy.

Epilepsy is a complex neurological disorder characterized by recurrent seizures affecting millions of people worldwide. Despite advances in drug therapy, a significant proportion of patients remain resistant to conventional antiepileptic drugs (AEDs) due to challenges such as impermeability of the blood-brain barrier (BBB), multidrug resistance, and multifaceted epileptogenesis. Nanotechnology offers promising strategies to overcome these barriers by enhancing drug delivery across the BBB, improving target specificity and minimizing systemic side effects.

Toward High-Performance Pure-Green Tandem Organic Light-Emitting Diodes by Employing Barrier-Free Strategies in the Charge Generation Layer.

Charge generation layers (CGLs) play crucial roles in determining the electroluminescence (EL) performance of tandem organic light-emitting diodes (OLEDs). However, acquiring negligible voltage drops across the CGL unit and high-efficiency multiplications remains challenging. Here, we propose barrier-free strategies to compose a high-performance p-i-n type CGL intermediate by introducing a Yb/HI-9 modification at the heterojunction and a novel n-dopant, Yb:1,3-bis(9-phenyl-1,10-phenanthrolin-2-yl)benzene (mdPPhen), as the n-CGL.

BioMedGraphica: An All-in-One Platform for Biomedical Prior Knowledge and Omic Signaling Graph Generation.

Artificial intelligence (AI) is revolutionizing scientific discovery because of its super capability, following the neural scaling laws, to integrate and analyze large-scale datasets to mine knowledge. Foundation models, large language models (LLMs) and large vision models (LVMs), are among the most important foundations paving the way for general AI by pre-training on massive domain-specific datasets. Different from the well annotated, formatted and integrated large textual and image datasets for LLMs and LVMs, biomedical knowledge and datasets are fragmented with data scattered across publications and inconsistent databases that often use diverse nomenclature systems in the field of AI for Precision Health and Medicine (AI4PHM).

Organic Crystal with Anti-Stokes Photoluminescent Excitation and Thermally Activated Delayed Fluorescence Features.

Thermal activation process utilizes environmental thermal energy to help supplement energy for the nonspontaneous energy-consuming upconversion physical transitions with positive free energy change (ΔG>0). Reverse intersystem crossing (rISC) and hot band absorption are two kinds of thermal activation transitions. Thermally activated delayed fluorescence (TADF) materials with rISC have significantly propelled advancements in organic semiconductors.

mosGraphGen: a novel tool to generate multi-omics signaling graphs to facilitate integrative and interpretable graph AI model development.

Motivation: Multi-omics data, i.e. genomics, epigenomics, transcriptomics, proteomics, characterize cellular complex signaling systems from multi-level and multi-view and provide a holistic view of complex cellular signaling pathways.

Comparative analysis of the therapeutic efficacy of remimazolam tosylate and propofol in older adults undergoing painless endoscopic retrograde cholangiopancreatography.

Objective: The aim of this study is to conduct a comparative analysis of the therapeutic outcomes associated with the administration of remimazolam and propofol during painless endoscopic retrograde cholangiopancreatography (ERCP) procedures in older adults.

Methods: A total of 140 older adults who underwent elective painless ERCP were randomly assigned to two groups using the random number table method: the remimazolam group and the propofol group, each consisting of 70 patients. In the remimazolam group, anesthesia was administered using a combination of remimazolam and opioids, while in the propofol group, a combination of propofol and opioids was used.

M3NetFlow: A novel multi-scale multi-hop graph AI model for integrative multi-omic data analysis.

Multi-omic data-driven studies, characterizing complex disease signaling system from multiple levels, are at the forefront of precision medicine and healthcare. The integration and interpretation of multi-omic data are essential for identifying molecular targets and deciphering core signaling pathways of complex diseases. However, it remains an open problem due the large number of biomarkers and complex interactions among them.

mosGraphFlow: a novel integrative graph AI model mining disease targets from multi-omic data.

Multi-omic data can better characterize complex cellular signaling pathways from multiple views compared to individual omic data. However, integrative multi-omic data analysis to rank key disease biomarkers and infer core signaling pathways remains an open problem. In this study, our novel contributions are that we developed a novel graph AI model, , for analyzing multi-omic signaling graphs (mosGraphs), 2) analyzed multi-omic mosGraph datasets of AD, and 3) identified, visualized and evaluated a set of AD associated signaling biomarkers and network.

Using DeepSignalingFlow to mine signaling flows interpreting mechanism of synergy of cocktails.

Complex signaling pathways are believed to be responsible for drug resistance. Drug combinations perturbing multiple signaling targets have the potential to reduce drug resistance. The large-scale multi-omic datasets and experimental drug combination synergistic score data are valuable resources to study mechanisms of synergy (MoS) to guide the development of precision drug combinations.

mosGraphGPT: a foundation model for multi-omic signaling graphs using generative AI.

Generative pretrained models represent a significant advancement in natural language processing and computer vision, which can generate coherent and contextually relevant content based on the pre-training on large general datasets and fine-tune for specific tasks. Building foundation models using large scale omic data is promising to decode and understand the complex signaling language patterns within cells. Different from existing foundation models of omic data, we build a foundation model, , for multi-omic signaling (mos) graphs, in which the multi-omic data was integrated and interpreted using a multi-level signaling graph.

circRNA6448-14/miR-455-3p/OTUB2 axis stimulates glycolysis and stemness of esophageal squamous cell carcinoma.

Background: Esophageal squamous cell carcinoma (ESCC) is a gastrointestinal malignancy with high incidence. This study aimed to reveal the complete circRNA-miRNA-mRNA regulatory network in ESCC and validate its function mechanism.

Method: Expression of OTU Domain-Containing Ubiquitin Aldehyde-Binding Protein 2 (OTUB2) in ESCC was analyzed by bioinformatics to find the binding sites between circRNA6448-14 and miR-455-3p, as well as miR-455-3p and OTUB2.

mosGraphGen: a novel tool to generate multi-omics signaling graphs to facilitate integrative and interpretable graph AI model development.

Multi-omics data, i.e., genomics, epigenomics, transcriptomics, proteomics, characterize cellular complex signaling systems from multi-level and multi-view and provide a holistic view of complex cellular signaling pathways.

The influence of Life's Essential 8 on the link between socioeconomic status and depression in adults: a mediation analysis.

Background: Individuals with low socioeconomic status (SES) are at a higher risk of developing depression. However, evidence on the role of cardiovascular health (CVH) in this chain is sparse and limited. The purpose of this research was to assess the mediating role of Life's Essential 8 (LE8), a recently updated measurement of CVH, in the association between SES and depression according to a nationally representative sample of adults.

Maximum correentropy-based robust Square-root Cubature Kalman Filter for vehicular cooperative navigation.

As the core method of cooperative navigation, relative positioning plays a key role in realizing intelligent vehicle driving and vehicle self-assembling network collaboration algorithms. However, when the contamination rate of measurement noise is high, the performance of filtering will be seriously affected. To better address the filtering performance degradation problem due to noise contamination, this paper proposes a vehicular cooperative localization method based on the Maximum Correentropy Robust Square-root Cubature Kalman Filter (MCSCKF).

Boosting interpersonal emotion regulation through facial imitation: functional neuroimaging foundations.

Empathic function, which is primarily manifested by facial imitation, is believed to play a pivotal role in interpersonal emotion regulation for mood reinstatement. To explore this association and its neural substrates, we performed a questionnaire survey (study l) to identify the relationship between empathy and interpersonal emotion regulation; and a task-mode fMRI study (study 2) to explore how facial imitation, as a fundamental component of empathic processes, promotes the interpersonal emotion regulation effect. Study 1 showed that affective empathy was positively correlated with interpersonal emotion regulation.

Static and dynamic functional connectome reveals reconfiguration profiles of whole-brain network across cognitive states.

Assessment of functional connectivity (FC) has revealed a great deal of knowledge about the macroscale spatiotemporal organization of the brain network. Recent studies found task-versus-rest network reconfigurations were crucial for cognitive functioning. However, brain network reconfiguration remains unclear among different cognitive states, considering both aggregate and time-resolved FC profiles.

Interpreting the Mechanism of Synergism for Drug Combinations Using Attention-Based Hierarchical Graph Pooling.

Synergistic drug combinations provide huge potentials to enhance therapeutic efficacy and to reduce adverse reactions. However, effective and synergistic drug combination prediction remains an open question because of the unknown causal disease signaling pathways. Though various deep learning (AI) models have been proposed to quantitatively predict the synergism of drug combinations, the major limitation of existing deep learning methods is that they are inherently not interpretable, which makes the conclusions of AI models untransparent to human experts, henceforth limiting the robustness of the model conclusion and the implementation ability of these models in real-world human-AI healthcare.

Dexmedetomidine Improves Anxiety-like Behaviors in Sleep-Deprived Mice by Inhibiting the p38/MSK1/NFκB Pathway and Reducing Inflammation and Oxidative Stress.

(1) Background: Sleep deprivation (SD) triggers a range of neuroinflammatory responses. Dexmedetomidine can improve sleep deprivation-induced anxiety by reducing neuroinflammatory response but the mechanism is unclear; (2) Methods: The sleep deprivation model was established by using an interference rod device. An open field test and an elevated plus maze test were used to detect the emotional behavior of mice.

Mining algorithm of accumulation sequence of unbalanced data based on probability matrix decomposition.

Due to the inherent characteristics of accumulation sequence of unbalanced data, the mining results of this kind of data are often affected by a large number of categories, resulting in the decline of mining performance. To solve the above problems, the performance of data cumulative sequence mining is optimized. The algorithm for mining cumulative sequence of unbalanced data based on probability matrix decomposition is studied.

Correction: Crystalline matrix-activated spin-forbidden transitions of engineered organic crystals.

Correction for 'Crystalline matrix-activated spin-forbidden transitions of engineered organic crystals' by Heming Zhang , , 2023, , 11102-11110, DOI: https://doi.org/10.1039/d3cp00187c.