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. DR mice model was constructed by streptozotocin injection. The pathological structure of retina was observed using hematoxylin-eosin staining. The enzyme-linked immunosorbent assay was applied to assess inflammatory factors, vascular endothelial growth factor (VEGF), and oxidative stress. The mRNA and protein expression levels were detected using quantitative real-time polymerase-chain reaction and Western blot. Cell counting kit and flow cytometry were employed to detect proliferation and apoptosis in high glucose-induced ARPE-19 cells. Results: Total 71 pyroptosis-related DEGs were screened. BIRC2, CXCL8, APAF1, PPARG, TP53, and CYCS were identified as hub genes of DR. APAF1 was selected as a potential regulator of DR, which was up-regulated in DR mice. APAF1 silencing alleviated retinopathy and inhibited pyroptosis in DR mice with decreased levels of inflammatory factors, VEGF, and oxidative stress. Moreover, APAF1 silencing promoted proliferation while inhibiting apoptosis and caspase-3/GSDME-dependent pyroptosis with a decrease in TNF-α, IL-1ß, IL-18, and lactate dehydrogenase in high glucose-induced ARPE-19 cells. Additionally, caspase-3 activator reversed the promotion effect on proliferation and inhibitory effect on apoptosis and pyroptosis after APAF1 silencing in high glucose-induced ARPE-19 cells. Conclusion: APAF1 is a novel biomarker for DR and APAF1 silencing inhibits the development of DR by suppressing caspase-3/GSDME-dependent pyroptosis.

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 NOx with NH3 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. We demonstrate that GPT-4, equipped with this new OSCoT prompting strategy, outperforms the other two settings and accurately predicts experimental outcomes and provides intuitive reasoning for its predictions.

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. Our approach successfully predicts their first-principles conduction and VBs by expressing them as a linear combination of the Frontier molecular orbitals localized on the COF fragments. We demonstrate that our method allows for the rapid exploration of the impact of chemical modifications on the band structures of COFs, making it highly suitable for further application in the quest to discover new functional materials.

Knowledge-Driven Experimental Discovery of Ce-Based Metal Oxide Composites for Selective Catalytic Reduction of NOx with NH3 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 (NH3-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. By virtual screening, this framework precisely pinpointed a CeO2-MoO3-Fe2O3 catalyst with superior NOx conversion, N2 selectivity, and resistance to H2O and SO2, as confirmed by empirical evaluations. Subsequent characterization affirmed its favorable structural, chemical bulk properties and reaction mechanism. Demonstrating the efficacy of combining knowledge-driven techniques with experimental validation and analysis, our strategy charts a course for analogous catalyst discoveries.

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 via random alterations in atomic identities. We discuss the challenges and limitations inherent to the use of GPT-3 in molecular machine-learning tasks and suggest potential directions for future research and improvements to address these issues.

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. This involved 736 experiments in all, out of possible 4320 ternary combinations. The top ten most active TOHPs discovered using this strategy showed outstanding sacrificial hydrogen production rates of more than 500 mmol g-1 h-1, with the most active ternary material reaching a rate of 749.8 mmol g-1 h-1 under 1 sun illumination. These rates of photocatalytic hydrogen generation are among the highest reported for organic photocatalysts in the literature.

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. Ophthalmoscopic examination revealed a subretinal lesion in the superonasal peripapillary region with several punctate hemorrhages. Optical coherence tomography showed a crater-like choroidal protuberance, associated with retinal pigment epithelium rupture and full-thickness retinal edema in the involved area. Indocyanine green angiography demonstrated a broad hypofluorescent lesion in the choroid. The patient was diagnosed with choroiditis. Subsequently, metagenomic next-generation sequencing revealed HHV-6B and HHV-7 DNA in the aqueous humor. Therefore, antiviral therapy was initiated. The patient experienced resolution of all symptoms and signs after treatment with intravenous foscarnet and oral acyclovir. The findings in this case indicate that HHV-6 and HHV-7 can cause ocular infection, particularly in immunocompromised patients.

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. METHODS: The effects and mechanism of Roflu against RIR injury were investigated in C57BL/6J mice and the BV2 cell line. We used quantitative real-time PCR and enzyme-linked immunosorbent assay to examine the levels of inflammatory factors. Furthermore, haematoxylin and eosin and immunofluorescence (IF) stainings were used to assess the morphology of the retina and the states of MGs and RGCs. Reactive oxygen species (ROS) levels were examined using a ROS assay kit, while whole-genome sequencing analysis was conducted to identify altered pathways and molecules. Western blotting and IF staining were used to quantify the proteins associated with the nuclear factor erythroid 2-related factor 2 (Nrf2)/stimulator of interferon gene (STING)/nuclear factor kappa beta (NF-κB) pathway. RESULTS: MG polarisation includes the pro-inflammatory and neurotoxic M1 phenotype as well as the anti-inflammatory and neuroprotective M2 phenotype. Roflu significantly attenuated MG activation and contributed to a shift in the MG phenotype from M1 to M2. Moreover, Roflu decreased ROS release and increased heme oxygenase 1 and NAD(P)H quinone oxidoreductase 1 expression. In vitro and in vivo experiments validated that Roflu exerted its neuroprotective effects primarily by upregulating the Nrf2/STING/NF-κB pathway. However, these effects were abrogated when the Nrf2 expression was inhibited by pharmacological or genetic manipulation. CONCLUSIONS: Roflu suppressed RIR-induced neuroinflammation by driving the shift of MG polarisation from M1 to M2 phenotype, which was mediated by the upregulation of the Nrf2/STING/NK-κB 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. Previously, we combined CSP with structure-property predictions to generate energy-structure-function (ESF) maps for a series of triptycene-based molecules with quinoxaline groups. From these ESF maps, triptycene trisquinoxalinedione (TH5) was predicted to form a previously unknown low-energy HOF (TH5-A) with a remarkably low density of 0.374 g cm-3 and three-dimensional (3D) pores. Here, we demonstrate the reliability of those ESF maps by discovering this TH5-A polymorph experimentally. This material has a high accessible surface area of 3,284 m2 g-1 , as measured by nitrogen adsorption, making it one of the most porous HOFs reported to date.

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-1 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. The 'static' picture thus obtained was augmented by probing the nonadiabatic dynamics of the molecules beyond the Born-Oppenheimer approximation, revealing a different picture of exciton recombination and relaxation for molecule 3. Our results suggest that slow exciton recombination, fast relaxation to the lowest-energy excited state, and a shorter charge transfer distance between the photocatalyst and the metal cocatalyst are important features that contribute to the photocatalytic hydrogen evolution activity of 1 and 2, and may partly rationalize the observed inactivity of 3, in addition to its lower light absorption profile.

Pregabalin Mediates Retinal Ganglion Cell Survival From Retinal Ischemia/Reperfusion Injury Via the Akt/GSK3ß/ß-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. In this study, we sought to investigate whether PGB could protect RGCs from mitochondria-related apoptosis induced by RIR and explore the possible mechanisms. Methods: C57BL/6J mice and primary RGCs were pretreated with PGB prior to ischemia/reperfusion modeling. The retinal structure and cell morphology were assessed by immunochemical assays and optical coherence tomography. CCK8 was used to assay cell viability, and an electroretinogram was performed to detect RGC function. Mitochondrial damage was assessed by a reactive oxygen species (ROS) assay kit and transmission electron microscopy. Western blot and immunofluorescence assays quantified the expression of proteins associated with the Akt/GSK3ß/ß-catenin pathway. Results: Treatment with PGB increased the viability of RGCs in vitro. Consistently, PGB preserved the normal thickness of the retina, upregulated Bcl-2, reduced the ratio of cleaved caspase-3/caspase-3 and the expression of Bax in vivo. Meanwhile, PGB improved mitochondrial structure and prevented excessive ROS production. Moreover, PGB restored the amplitudes of oscillatory potentials and photopic negative responses following RIR. The mechanisms underlying its neuroprotective effects were attributed to upregulation of the Akt/GSK3ß/ß-catenin pathway. However, PGB-mediated neuroprotection was suppressed when using MK2206 (an Akt inhibitor), whereas it was preserved when treated with TWS119 (a GSK3ß inhibitor). Conclusions: PGB exerts a protective effect against RGC apoptosis induced by RIR injury, mediated by the Akt/GSK3ß/ß-catenin pathway.

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. These conditions enable volatile and gaseous alkenes and alkynes to be used in IV, leading to sustainable alternatives for environmentally harmful plastics that were hitherto inaccessible. Density functional theory calculations reveal different energy barriers for thermal, catalytic and photoinduced IV processes. This protocol circumvents the long curing times that are common in IV, generates no H2S by-products, and produces high-molecular-weight polymers (up to 460,000 g mol-1) with almost 100% atom economy. This photoinduced IV strategy advances both the fundamental chemistry of IV and its potential industrial application to generate materials from waste feedstocks.

Mapping the Porous and Chemical Structure-Function Relationships of Trace CH3I Capture by Metal-Organic Frameworks using Machine Learning.

Large-scale computational screening has become an indispensable tool for functional materials discovery. It, however, remains a challenge to adequately interrogate the large amount of data generated by a screening study. Here, we computationally screened 1087 metal-organic frameworks (MOFs), from the CoRE MOF 2014 database, for capturing trace amounts (300 ppmv) of methyl iodide (CH3I); as a primary representative of organic iodides, CH3129I is one of the most difficult radioactive contaminants to separate. Furthermore, we demonstrate a simple and general approach for mapping and interrogating the high-dimensional structure-function data obtained by high-throughput screening; this involves learning two-dimensional embeddings of the high-dimensional data by applying unsupervised learning to encoded structural and chemical features of MOFs. The resulting various porous and chemical structure-function maps are human-interpretable, revealing not only top-performing MOFs but also complex structure-function correlations that are hidden when inspecting individual MOF features. These maps also alleviate the need of laborious visual inspection of a large number of MOFs by clustering similar MOFs, per the encoding features, into defined regions on the map. We also show that these structure-function maps are amenable to supervised classification of the performances of MOFs for trace CH3I capture. We further show that the machine-learning models trained on the 1087 CoRE MOFs can be used to predict an unseen set of 250 MOFs randomly selected from a different MOF database, achieving high prediction accuracies.

A deep-learning system predicts glaucoma incidence and progression using retinal photographs.

BackgroundDeep learning has been widely used for glaucoma diagnosis. However, there is no clinically validated algorithm for glaucoma incidence and progression prediction. This study aims to develop a clinically feasible deep-learning system for predicting and stratifying the risk of glaucoma onset and progression based on color fundus photographs (CFPs), with clinical validation of performance in external population cohorts.MethodsWe established data sets of CFPs and visual fields collected from longitudinal cohorts. The mean follow-up duration was 3 to 5 years across the data sets. Artificial intelligence (AI) models were developed to predict future glaucoma incidence and progression based on the CFPs of 17,497 eyes in 9346 patients. The area under the receiver operating characteristic (AUROC) curve, sensitivity, and specificity of the AI models were calculated with reference to the labels provided by experienced ophthalmologists. Incidence and progression of glaucoma were determined based on longitudinal CFP images or visual fields, respectively.ResultsThe AI model to predict glaucoma incidence achieved an AUROC of 0.90 (0.81-0.99) in the validation set and demonstrated good generalizability, with AUROCs of 0.89 (0.83-0.95) and 0.88 (0.79-0.97) in external test sets 1 and 2, respectively. The AI model to predict glaucoma progression achieved an AUROC of 0.91 (0.88-0.94) in the validation set, and also demonstrated outstanding predictive performance with AUROCs of 0.87 (0.81-0.92) and 0.88 (0.83-0.94) in external test sets 1 and 2, respectively.ConclusionOur study demonstrates the feasibility of deep-learning algorithms in the early detection and prediction of glaucoma progression.FUNDINGNational Natural Science Foundation of China (NSFC); the High-level Hospital Construction Project, Zhongshan Ophthalmic Center, Sun Yat-sen University; the Science and Technology Program of Guangzhou, China (2021), the Science and Technology Development Fund (FDCT) of Macau, and FDCT-NSFC.

How Reproducible are Surface Areas Calculated from the BET Equation?

Porosity and surface area analysis play a prominent role in modern materials science. At the heart of this sits the Brunauer-Emmett-Teller (BET) theory, which has been a remarkably successful contribution to the field of materials science. The BET method was developed in the 1930s for open surfaces but is now the most widely used metric for the estimation of surface areas of micro- and mesoporous materials. Despite its widespread use, the calculation of BET surface areas causes a spread in reported areas, resulting in reproducibility problems in both academia and industry. To prove this, for this analysis, 18 already-measured raw adsorption isotherms were provided to sixty-one labs, who were asked to calculate the corresponding BET areas. This round-robin exercise resulted in a wide range of values. Here, the reproducibility of BET area determination from identical isotherms is demonstrated to be a largely ignored issue, raising critical concerns over the reliability of reported BET areas. To solve this major issue, a new computational approach to accurately and systematically determine the BET area of nanoporous materials is developed. The software, called "BET surface identification" (BETSI), expands on the well-known Rouquerol criteria and makes an unambiguous BET area assignment possible.

Reconstructed covalent organic frameworks.

Covalent organic frameworks (COFs) are distinguished from other organic polymers by their crystallinity1-3, but it remains challenging to obtain robust, highly crystalline COFs because the framework-forming reactions are poorly reversible4,5. More reversible chemistry can improve crystallinity6-9, but this typically yields COFs with poor physicochemical stability and limited application scope5. Here we report a general and scalable protocol to prepare robust, highly crystalline imine COFs, based on an unexpected framework reconstruction. In contrast to standard approaches in which monomers are initially randomly aligned, our method involves the pre-organization of monomers using a reversible and removable covalent tether, followed by confined polymerization. This reconstruction route produces reconstructed COFs with greatly enhanced crystallinity and much higher porosity by means of a simple vacuum-free synthetic procedure. The increased crystallinity in the reconstructed COFs improves charge carrier transport, leading to sacrificial photocatalytic hydrogen evolution rates of up to 27.98 mmol h-1 g-1. This nanoconfinement-assisted reconstruction strategy is a step towards programming function in organic materials through atomistic structural control.

A Cubic 3D Covalent Organic Framework with nbo Topology.

The synthesis of three-dimensional (3D) covalent organic frameworks (COFs) requires high-connectivity polyhedral building blocks or the controlled alignment of building blocks. Here, we use the latter strategy to assemble square-planar cobalt(II) phthalocyanine (PcCo) units into the nbo topology by using tetrahedral spiroborate (SPB) linkages that were chosen to provide the necessary 90° dihedral angles between neighboring PcCo units. This yields a porous 3D COF, SPB-COF-DBA, with a noninterpenetrated nbo topology. SPB-COF-DBA shows high crystallinity and long-range order, with 11 resolved diffraction peaks in the experimental powder X-ray diffraction (PXRD) pattern. This well-ordered crystal lattice can also be imaged by using high-resolution transmission electron microscopy (HR-TEM). SPB-COF-DBA has cubic pores and exhibits permanent porosity with a Brunauer-Emmett-Teller (BET) surface area of 1726 m2 g-1.

Combining machine learning and high-throughput experimentation to discover photocatalytically active organic molecules.

Light-absorbing organic molecules are useful components in photocatalysts, but it is difficult to formulate reliable structure-property design rules. More than 100 million unique chemical compounds are documented in the PubChem database, and a significant sub-set of these are π-conjugated, light-absorbing molecules that might in principle act as photocatalysts. Nature has used natural selection to evolve photosynthetic assemblies; by contrast, our ability to navigate the enormous potential search space of organic photocatalysts in the laboratory is limited. Here, we integrate experiment, computation, and machine learning to address this challenge. A library of 572 aromatic organic molecules was assembled with diverse compositions and structures, selected on the basis of availability in our laboratory, rather than more sophisticated criteria. This training library was then assessed experimentally for sacrificial photocatalytic hydrogen evolution using a high-throughput, automated method. Quantum chemical calculations and machine learning were used to visualise, interpret, and ultimately to predict the photocatalytic activities of these molecules, covering a much broader chemical space than for previous polymer photocatalyst libraries. By applying unsupervised learning to the molecular structures, we identified structural features that were common in molecules with high catalytic activity. Further analysis using calculated molecular descriptors within a suite of supervised classification algorithms revealed that light absorption, exciton electron affinity, electron affinity, exciton binding energy, and singlet-triplet energy gap had correlations with the photocatalytic performance. These trained predictive models can be used in future studies as filters to deprioritise or discard would-be low-activity candidate molecules from experiments, and to prioritize more favourable candidates. As a demonstration, we used virtual in silico experiments to show that it was possible to halve the experimental cost of finding 50% of the most active photocatalysts by using the machine learning model as an experimental advisor. We further showed that the ML advisor trained on the 572-molecule library could be used to make predictions for an unseen set of 96 molecules, achieving equivalent predictive accuracies to those in the initial training set. This marks a step toward the machine-learning assisted discovery of molecular organic photocatalysts and the approach might also be applied to problems beyond photocatalytic hydrogen evolution, such as CO2 reduction and photoredox chemistry.

Accelerating computational discovery of porous solids through improved navigation of energy-structure-function maps.

While energy-structure-function (ESF) maps are a powerful new tool for in silico materials design, the cost of acquiring an ESF map for many properties is too high for routine integration into high-throughput virtual screening workflows. Here, we propose the next evolution of the ESF map. This uses parallel Bayesian optimization to selectively acquire energy and property data, generating the same levels of insight at a fraction of the computational cost. We use this approach to obtain a two orders of magnitude speedup on an ESF study that focused on the discovery of molecular crystals for methane capture, saving more than 500,000 central processing unit hours from the original protocol. By accelerating the acquisition of insight from ESF maps, we pave the way for the use of these maps in automated ultrahigh-throughput screening pipelines by greatly reducing the opportunity risk associated with the choice of system to calculate.

In Silico Tuning of the Pore Surface Functionality in Al-MOFs for Trace CH3I Capture.

Aluminum (Al)-based metal-organic frameworks (MOFs) have been shown to have good stability toward γ irradiation, making them promising candidates for durable adsorbents for capturing volatile radioactive nuclides. In this work, we studied a series of existing Al-MOFs to capture trace radioactive organic iodide (ROI) from a gas composition (100 ppm CH3I, 400 ppm CO2, 21% O2, and 78% N2) resembling the off-gas composition from reprocessing the used nuclear fuel using Grand canonical Monte Carlo (GCMC) simulations and density functional theory (DFT) calculations. Based on the results and understanding established from studying the existing Al-MOFs, we proceed by functionalizing the top-performing CAU-11 with different functional groups to propose better MOFs for ROI capture. Our study suggests that extraordinary ROI adsorption and separation capability could be realized by -SO3H functionalization in CAU-11. It was mainly owing to the joint effect of the enhanced pore surface polarity arising from -SO3H functionalization and the µ-OH group of CAU-11.