Comparative Effectiveness of Angiotensin-Converting Enzyme Inhibitors Versus Angiotensin Receptor Blockers: Multidatabase Target Trial Emulation Studies.

Background: ACE (angiotensin-converting enzyme) inhibitors and ARBs (angiotensin receptor blockers) are equally recommended as first-line treatment for cardiovascular and renal protection in clinical practice. Evidence on the comparative effectiveness of both drugs on long-term death is inconclusive.

Methods: This multidatabase cohort study used a target trial emulation framework based on the UK Biobank database and the China Renal Data System.

Intelligent understanding of spectra: from structural elucidation to property design.

Spectroscopy serves as a bridge between experimental observations and quantum mechanical principles, linking molecular microstructure to macroscopic material properties. Despite its central importance, establishing quantitative structure-property relationships from spectral data remains challenging, typically requiring expensive quantum chemistry calculations and specialized expertise. The integration of artificial intelligence (AI) with spectroscopy presents a transformative opportunity to overcome these limitations.

Bottom-up computational design of shape-selective organic macrocycles for humid CO capture.

The capture of CO emissions using porous solids is challenging because polar water molecules bind more strongly in most materials than non-polar CO molecules. This is a challenge for both flue gas capture and for direct air capture alike. Here we develop a bottom-up computational screening workflow to calculate the binding energy of 27,446 diverse molecular fragments with both CO and water.

Selective CO Adsorption in Ultrahydrophobic Molecular Pyrene Frameworks by Computational Design.

The separation of carbon dioxide from industrial flue gas streams using porous materials is often thwarted by humidity. Most porous sorbents adsorb water more effectively than CO. Hence, water can out-compete CO for adsorption sites, lowering the working CO sorption capacity and increasing sorbent regeneration costs.

Design of circularly polarized phosphorescence materials guided by transfer learning.

It is highly desirable that artificial circularly polarized phosphorescent materials with high luminescence asymmetry factor (g), narrowband emission and tunable chiral phosphorescent performance can be constructed. Especially, precise control and simultaneous independent switching of circularly polarized fluorescent and phosphorescent performance for the same molecules remain a formidable challenge. Herein, we propose a strategy to customized design of circularly polarized phosphorescent materials based on large language models and transfer learning methods, which not only enables efficient identification of suitable synthesis precursors, but also provides valuable guidance for experimental procedures.

Discovery of a molecular adsorbent for efficient CO/CH separation using a computation-ready experimental database of porous molecular materials.

The development and sharing of computational databases for metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) have significantly accelerated the exploration and application of these materials. Recently, molecular materials have emerged as a notable subclass of porous materials, characterized by their crystallinity, modularity, and processability. Among these, macrocycles and cages stand out as representative molecules.

Quantifying interactions in the active encounter complex of frustrated Lewis pairs.

Sustainable catalysts based on main-group elements, such as frustrated Lewis pairs (FLPs), have emerged as alternatives to precious metal systems. The initial reaction of the Lewis acid, Lewis base and small molecule (e.g.

A Multiagent-Driven Robotic AI Chemist Enabling Autonomous Chemical Research On Demand.

The successful integration of large language models (LLMs) into laboratory workflows has demonstrated robust capabilities in natural language processing, autonomous task execution, and collaborative problem-solving. This offers an exciting opportunity to realize the dream of autonomous chemical research on demand. Here, we report a robotic AI chemist powered by a hierarchical multiagent system, ChemAgents, based on an on-board Llama-3.

Transformer-Based Models for Predicting Molecular Structures from Infrared Spectra Using Patch-Based Self-Attention.

Infrared (IR) spectroscopy, a type of vibrational spectroscopy, provides extensive molecular structure details and is a highly effective technique for chemists to determine molecular structures. However, analyzing experimental spectra has always been challenging due to the specialized knowledge required and the variability of spectra under different experimental conditions. Here, we propose a transformer-based model with a patch-based self-attention spectrum embedding layer, designed to prevent the loss of spectral information while maintaining simplicity and effectiveness.

A machine learned potential for investigating single crystal to single crystal transformations in complex organic molecular systems.

The packing of organic molecular crystals is often dominated by weak non-covalent interactions, making their rearrangement under external stimuli challenging to understand. We investigate a pressure-induced single-crystal-to-single-crystal (SCSC) transformation between two polymorphs of 2,4,5-triiodo-1-imidazole using machine learning potentials. This process involves the rearrangement of halogen and hydrogen bonds combined with proton transfer within a complex solid-state system.

NR2E3 inhibits the inflammation and apoptosis in diabetic retinopathy by regulating the AHR/IL-17A signaling pathway.

Diabetic retinopathy (DR) is the most prevalent microvascular complication of diabetes mellitus, and it is the primary cause of blindness in the working-age population worldwide. Nevertheless, the pathogenic molecular mechanisms of DR remain elusive. Hub genes were identified through bioinformatics analysis in the GSE102485 and GSE60436 datasets.

Sequential closed-loop Bayesian optimization as a guide for organic molecular metallophotocatalyst formulation discovery.

Conjugated organic photoredox catalysts (OPCs) can promote a wide range of chemical transformations. It is challenging to predict the catalytic activities of OPCs from first principles, either by expert knowledge or by using a priori calculations, as catalyst activity depends on a complex range of interrelated properties. Organic photocatalysts and other catalyst systems have often been discovered by a mixture of design and trial and error.

Screening potential dye sensitizers for water splitting photocatalysts using a genetic algorithm.

Addressing the global fossil energy crisis necessitates the efficient utilization of sustainable energy sources. Hydrogen, a green fuel, can be generated using sunlight, water, and a photocatalyst. Employing sensitizers holds promise for enhancing photocatalyst performance, enabling high rates of hydrogen evolution through increased visible light absorption.

APAF1 Silencing Ameliorates Diabetic Retinopathy by Suppressing Inflammation, Oxidative Stress, and Caspase-3/GSDME-Dependent Pyroptosis.

Objective: Diabetic retinopathy (DR) can cause permanent blindness with unstated pathogenesis. We aim to find novel biomarkers and explore the mechanism of apoptotic protease activating factor 1 (APAF1) in DR.

Methods: Differential expression genes (DEGs) were screened based on GSE60436 dataset to find hub genes involved in pyroptosis after comprehensive bioinformatics analysis.

Analysis and prediction in SCR experiments using GPT-4 with an effective chain-of-thought prompting strategy.

This study explores the use of large language models (LLMs) in interpreting and predicting experimental outcomes based on given experimental variables, leveraging the human-like reasoning and inference capabilities of LLMs, using selective catalytic reduction of NO with NH as a case study. We implement the chain of thought (CoT) concept to formulate logical steps for uncovering connections within the data, introducing an "Ordered-and-Structured" CoT (OSCoT) prompting strategy. We compare the OSCoT strategy with the more conventional "One-Pot" CoT (OPCoT) approach and with human experts.

Intuitive and Efficient Approach to Determine the Band Structure of Covalent Organic Frameworks from Their Chemical Constituents.

The optical, electronic, and (photo) catalytic properties of covalent organic frameworks (COFs) are largely determined by their electronic structure and, specifically, by their Frontier conduction and valence bands (VBs). In this work, we establish a transparent relationship between the periodic electronic structure of the COFs and the orbital characteristics of their individual molecular building units, a relationship that is challenging to unravel through conventional solid-state calculations. As a demonstration, we applied our method to five COFs with distinct framework topologies.

Knowledge-Driven Experimental Discovery of Ce-Based Metal Oxide Composites for Selective Catalytic Reduction of NO with NH through Interpretable Machine Learning.

Mining the scientific literature, combined with data-driven methods, may assist in the identification of optimized catalysts. In this paper, we employed interpretable machine learning to discover ternary metal oxides capable of selective catalytic reduction of nitrogen oxides with ammonia (NH-SCR). Specifically, we devised a machine learning framework utilizing extreme gradient boosting (XGB), identified for its optimal performance, and SHapley Additive exPlanations (SHAP) to evaluate a curated database of 5654 distinct metal oxide composite catalytic systems containing cerium (Ce) element, with records of catalyst composition and preparation and reaction conditions.

Fine-tuning GPT-3 for machine learning electronic and functional properties of organic molecules.

We evaluate the effectiveness of fine-tuning GPT-3 for the prediction of electronic and functional properties of organic molecules. Our findings show that fine-tuned GPT-3 can successfully identify and distinguish between chemically meaningful patterns, and discern subtle differences among them, exhibiting robust predictive performance for the prediction of molecular properties. We focus on assessing the fine-tuned models' resilience to information loss, resulting from the absence of atoms or chemical groups, and to noise that we introduce random alterations in atomic identities.

Machine Learning Accelerated Exploration of Ternary Organic Heterojunction Photocatalysts for Sacrificial Hydrogen Evolution.

Donor-acceptor heterojunctions in organic photocatalysts can provide enhanced exciton dissociation and charge separation, thereby improving the photocatalytic activity. However, the wide choice of possible donors and acceptors poses a challenge for the rational design of organic heterojunction photocatalysts, particularly for large ternary phase spaces. We accelerated the exploration of ternary organic heterojunction photocatalysts (TOHP) by using a combination of machine learning and high-throughput experimental screening.

HHV-6B- and HHV-7-associated choroiditis secondary to acute myelogenous leukemia: a case report.

We report a case of human herpes virus 6 (HHV-6)- and human herpes virus 7 (HHV-7)-associated choroiditis in an immunocompromised woman. A 42-year-old Chinese woman with a history of acute myelogenous leukemia presented with blurred vision and black floaters in her right eye. Anterior segment examination findings were normal.

Roflumilast attenuates neuroinflammation post retinal ischemia/reperfusion injury by regulating microglia phenotype via the Nrf2/STING/NF-κB pathway.

Purpose: The abnormal polarisation of microglial cells (MGs) following retinal ischemia/reperfusion (RIR) initiates neuroinflammation and progressive death of retinal ganglion cells (RGCs), causing increasingly severe and irreversible visual dysfunction. Roflumilast (Roflu) is a promising candidate for treating neuroinflammatory diseases. This study aimed to explore whether Roflu displayed a cytoprotective effect against RIR-induced neuroinflammation and to characterise the underlying signalling pathway.

Experimental Confirmation of a Predicted Porous Hydrogen-Bonded Organic Framework.

Hydrogen-bonded organic frameworks (HOFs) with low densities and high porosities are rare and challenging to design because most molecules have a strong energetic preference for close packing. Crystal structure prediction (CSP) can rank the crystal packings available to an organic molecule based on their relative lattice energies. This has become a powerful tool for the a priori design of porous molecular crystals.

Investigating the factors that influence sacrificial hydrogen evolution activity for three structurally-related molecular photocatalysts: thermodynamic driving force, excited-state dynamics, and surface interaction with cocatalysts.

The design of molecular organic photocatalysts for reactions such as water splitting requires consideration of factors that go beyond electronic band gap and thermodynamic driving forces. Here, we carried out a theoretical investigation of three molecular photocatalysts (1-3) that are structurally similar but that show different hydrogen evolution activities (25, 23 & 0 μmol h for 1-3, respectively). We used density functional theory (DFT) and time-dependent DFT calculations to evaluate the molecules' optoelectronic properties, such as ionization potential, electron affinity, and exciton potentials, as well as the interaction between the molecular photocatalysts and an idealized platinum cocatalyst surface.

Pregabalin Mediates Retinal Ganglion Cell Survival From Retinal Ischemia/Reperfusion Injury Via the Akt/GSK3β/β-Catenin Signaling Pathway.

Purpose: Progressive retinal ganglion cell (RGC) loss induced by retinal ischemia/reperfusion (RIR) injury leads to irreversible visual impairment. Pregabalin (PGB) is a promising drug for neurodegenerative diseases. However, with regard to RGC survival, its specific role and exact mechanism after RIR injury remain unclear.

Photoinduced inverse vulcanization.

The inverse vulcanization (IV) of elemental sulfur to generate sulfur-rich functional polymers has attracted much recent attention. However, the harsh reaction conditions required, even with metal catalysts, constrains the range of feasible crosslinkers. We report here a photoinduced IV that enables reaction at ambient temperatures, greatly broadening the scope for both substrates and products.