Laser-Patterning of Micromagnets for Immuno-Magnetophoretic Exosome Capture.

Efficient isolation and patterning of biomolecules is a vital step within sample preparation for biomolecular analysis, with numerous diagnostic and therapeutic applications. For exosomes, nanoscale lipid-bound biomolecules, efficient isolation is challenging due to their minute size and resultant behavior within biofluids. This study presents a method for the rapid isolation and patterning of magnetically tagged exosomes via rationally designed micromagnets. Micromagnet fabrication utilizes a novel, scalable, and high-throughput laser-based fabrication approach that enables patterning at microscale lateral resolution (<50 µm) without lithographic processing and is agnostic to micromagnet geometry. Laser-based processing allows for flexible and tunable device configurations, and herein magnetophoretic capture within both an open-air microwell and an enclosed microfluidic system is demonstrated. Patterned micromagnets enhance localized gradient fields throughout the fluid medium, resulting in rapid and high efficiency magnetophoretic separation, with capture efficiencies nearing 70% after just 1s within open-air microwells, and throughputs upward of 3 mL h-1 within enclosed microfluidic systems. Using this microchip architecture, immunomagnetic exosome isolation and patterning directly from undiluted plasma samples is further achieved. Lastly, a FEA-based modeling workflow is introduced to characterize and optimize micromagnet unit cells, simulating magnetophoretic capture zones for a given micromagnet geometry.

Revolutionizing GPCR-ligand predictions: DeepGPCR with experimental validation for high-precision drug discovery.

G-protein coupled receptors (GPCRs), crucial in various diseases, are targeted of over 40% of approved drugs. However, the reliable acquisition of experimental GPCRs structures is hindered by their lipid-embedded conformations. Traditional protein-ligand interaction models falter in GPCR-drug interactions, caused by limited and low-quality structures. Generalized models, trained on soluble protein-ligand pairs, are also inadequate. To address these issues, we developed two models, DeepGPCR_BC for binary classification and DeepGPCR_RG for affinity prediction. These models use non-structural GPCR-ligand interaction data, leveraging graph convolutional networks and mol2vec techniques to represent binding pockets and ligands as graphs. This approach significantly speeds up predictions while preserving critical physical-chemical and spatial information. In independent tests, DeepGPCR_BC surpassed Autodock Vina and Schrödinger Dock with an area under the curve of 0.72, accuracy of 0.68 and true positive rate of 0.73, whereas DeepGPCR_RG demonstrated a Pearson correlation of 0.39 and root mean squared error of 1.34. We applied these models to screen drug candidates for GPR35 (Q9HC97), yielding promising results with three (F545-1970, K297-0698, S948-0241) out of eight candidates. Furthermore, we also successfully obtained six active inhibitors for GLP-1R. Our GPCR-specific models pave the way for efficient and accurate large-scale virtual screening, potentially revolutionizing drug discovery in the GPCR field.

A deep learning based multi-model approach for predicting drug-like chemical compound's toxicity.

Ensuring the safety and efficacy of chemical compounds is crucial in small-molecule drug development. In the later stages of drug development, toxic compounds pose a significant challenge, losing valuable resources and time. Early and accurate prediction of compound toxicity using deep learning models offers a promising solution to mitigate these risks during drug discovery. In this study, we present the development of several deep-learning models aimed at evaluating different types of compound toxicity, including acute toxicity, carcinogenicity, hERG_cardiotoxicity (the human ether-a-go-go related gene caused cardiotoxicity), hepatotoxicity, and mutagenicity. To address the inherent variations in data size, label type, and distribution across different types of toxicity, we employed diverse training strategies. Our first approach involved utilizing a graph convolutional network (GCN) regression model to predict acute toxicity, which achieved notable performance with Pearson R 0.76, 0.74, and 0.65 for intraperitoneal, intravenous, and oral administration routes, respectively. Furthermore, we trained multiple GCN binary classification models, each tailored to a specific type of toxicity. These models exhibited high area under the curve (AUC) scores, with an impressive AUC of 0.69, 0.77, 0.88, and 0.79 for predicting carcinogenicity, hERG_cardiotoxicity, mutagenicity, and hepatotoxicity, respectively. Additionally, we have used the approved drug dataset to determine the appropriate threshold value for the prediction score in model usage. We integrated these models into a virtual screening pipeline to assess their effectiveness in identifying potential low-toxicity drug candidates. Our findings indicate that this deep learning approach has the potential to significantly reduce the cost and risk associated with drug development by expediting the selection of compounds with low toxicity profiles. Therefore, the models developed in this study hold promise as critical tools for early drug candidate screening and selection.

Exercise-Induced Reduction of IGF1R Sumoylation Attenuates Neuroinflammation in APP/PS1 Transgenic Mice.

INTRODUCTION: Alzheimer's Disease (AD), a progressive neurodegenerative disorder, is marked by cognitive deterioration and heightened neuroinflammation. The influence of Insulin-like Growth Factor 1 Receptor (IGF1R) and its post-translational modifications, especially sumoylation, is crucial in understanding the progression of AD and exploring novel therapeutic avenues. OBJECTIVES: This study investigates the impact of exercise on the sumoylation of IGF1R and its role in ameliorating AD symptoms in APP/PS1 mice, with a specific focus on neuroinflammation and innovative therapeutic strategies. METHODS: APP/PS1 mice were subjected to a regimen of moderate-intensity exercise. The investigation encompassed assessments of cognitive functions, alterations in hippocampal protein expressions, neuroinflammatory markers, and the effects of exercise on IGF1R and SUMO1 nuclear translocation. Additionally, the study evaluated the efficacy of KPT-330, a nuclear export inhibitor, as an alternative to exercise. RESULTS: Exercise notably enhanced cognitive functions in AD mice, possibly through modulations in hippocampal proteins, including Bcl-2 and BACE1. A decrease in neuroinflammatory markers such as IL-1ß, IL-6, and TNF-α was observed, indicative of reduced neuroinflammation. Exercise modulated the nuclear translocation of SUMO1 and IGF1R in the hippocampus, thereby facilitating neuronal regeneration. Mutant IGF1R (MT IGF1R), lacking SUMO1 modification sites, showed reduced SUMOylation, leading to diminished expression of pro-inflammatory cytokines and apoptosis. KPT-330 impeded the formation of the IGF1R/RanBP2/SUMO1 complex, thereby limiting IGF1R nuclear translocation, inflammation, and neuronal apoptosis, while enhancing cognitive functions and neuron proliferation. CONCLUSION: Moderate-intensity exercise effectively mitigates AD symptoms in mice, primarily by diminishing neuroinflammation, through the reduction of IGF1R Sumoylation. KPT-330, as a potential alternative to physical exercise, enhances the neuroprotective role of IGF1R by inhibiting SUMOylation through targeting XPO1, presenting a promising therapeutic strategy for AD.

Generative Adversarial Network Model to Classify Human Induced Pluripotent Stem Cell-Cardiomyocytes based on Maturation Level.

Objective: Our study develops a generative adversarial network (GAN)-based method that generates faithful synthetic image data of human cardiomyocytes at varying stages in their maturation process, as a tool to significantly enhance the classification accuracy of cells and ultimately assist the throughput of computational analysis of cellular structure and functions. Methods: Human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMs) were cultured on micropatterned collagen coated hydrogels of physiological stiffnesses to facilitate maturation and optical measurements were performed for their structural and functional analyses. Control groups were cultured on collagen coated glass well plates. These image recordings were used as the real data to train the GAN model. Results: The results show the GAN approach is able to replicate true features from the real data, and inclusion of such synthetic data significantly improves the classification accuracy compared to usage of only real experimental data that is often limited in scale and diversity. Conclusion: The proposed model outperformed four conventional machine learning algorithms with respect to improved data generalization ability and data classification accuracy by incorporating synthetic data. Significance: This work demonstrates the importance of integrating synthetic data in situations where there are limited sample sizes and thus, effectively addresses the challenges imposed by data availability.

Statin-mediated reduction in mitochondrial cholesterol primes an anti-inflammatory response in macrophages by upregulating Jmjd3.

Statins are known to be anti-inflammatory, but the mechanism remains poorly understood. Here, we show that macrophages, either treated with statin in vitro or from statin-treated mice, have reduced cholesterol levels and higher expression of Jmjd3, a H3K27me3 demethylase. We provide evidence that lowering cholesterol levels in macrophages suppresses the adenosine triphosphate (ATP) synthase in the inner mitochondrial membrane and changes the proton gradient in the mitochondria. This activates nuclear factor kappa-B (NF-κB) and Jmjd3 expression, which removes the repressive marker H3K27me3. Accordingly, the epigenome is altered by the cholesterol reduction. When subsequently challenged by the inflammatory stimulus lipopolysaccharide (M1), macrophages, either treated with statins in vitro or isolated from statin-fed mice, express lower levels proinflammatory cytokines than controls, while augmenting anti-inflammatory Il10 expression. On the other hand, when macrophages are alternatively activated by IL-4 (M2), statins promote the expression of Arg1, Ym1, and Mrc1. The enhanced expression is correlated with the statin-induced removal of H3K27me3 from these genes prior to activation. In addition, Jmjd3 and its demethylase activity are necessary for cholesterol to modulate both M1 and M2 activation. We conclude that upregulation of Jmjd3 is a key event for the anti-inflammatory function of statins on macrophages.

Endovascular coiling versus neurosurgical clipping in the management of aneurysmal subarachnoid haemorrhage in the elderly: a multicenter cohort study.

The comparability of endovascular coiling over neurosurgical clipping has not been firmly established in elderly patients with aneurysmal subarachnoid haemorrhage (aSAH). Data were obtained from all patients with aSAH aged ≥60 across three tertiary hospitals in Singapore from 2014 to 2019. Outcome measures included modified Rankin Scale (mRS) score at 3 and at 6 months, and in-hospital mortality. Of the 134 patients analyzed, 84 (62.7%) underwent coiling and 50 (37.3%) underwent clipping. The endovascular group showed a higher incidence of good mRS score 0-2 at 3 months (OR = 2.45 [95%CI:1.16-5.20];p = 0.018), and a lower incidence of in-hospital mortality (OR = 0.31 [95%CI:0.10-0.91];p = 0.026). There were no significant difference between the two treatment groups in terms of good mRS score at 6 months (OR = 1.98 [95%CI:0.97-4.04];p = 0.060). There were no significant differences in the incidence of complications, such as aneurysm rebleed, delayed hydrocephalus, delayed ischemic neurological deficit and venous thromboembolism between the two treatment groups. However, fewer patients in the coiling group developed large infarcts requiring decompressive craniectomy (OR = 0.32 [95%CI:0.12-0.90];p = 0.025). Age, admission WFNS score I-III, and coiling were independent predictors of good functional outcomes at 3 months. Only age and admission WFNS score I-III remained significant predictors of good functional outcomes at 6 months. Endovascular coiling, compared with neurosurgical clipping, is associated with significantly better short term outcomes in carefully selected elderly patients with aSAH. Maximal intervention is recommended for aSAH in the young elderly age group and those with favorable WFNS scores.

Substituent effects on the selectivity of ambimodal [6+4]/[4+2] cycloaddition.

In this work, we report a density functional theory (DFT) study and a dynamical trajectory study of substituent effects on the ambimodal [6+4]/[4+2] cycloaddition proposed for 1,3,5,10,12-cycloheptadecapentaene, referred to as cycloheptadecapentaene. The proposed cycloaddition proceeds through an ambimodal transition state, which results in both a [6+4] adduct a [4+2] adduct directly. The [6+4] adduct can be readily converted to the [4+2] adduct via a Cope rearrangement. We study the selectivity of the reaction with regard to the position of substituents, steric effects of substituents, and electronic effects of substituents. In the dynamical trajectory study, we find that nitro-substituted reactants lead to a new product from the ambimodal transition state via the hetero Diels-Alder reaction, and this new product can then be converted to a [4+2] adduct by a hetero [3, 3]-sigmatropic rearrangement. These results may provide insights for designing more bridged heterocyclic compounds.

Cathepsin B as a key regulator of ferroptosis in microglia following intracerebral hemorrhage.

Intracerebral hemorrhage (ICH) is a subtype of stroke marked by elevated mortality and disability rates. Recently, mounting evidence suggests a significant role of ferroptosis in the pathogenesis of ICH. Through a combination of bioinformatics analysis and basic experiments, our goal is to identify the primary cell types and key molecules implicated in ferroptosis post-ICH. This aims to propel the advancement of ferroptosis research, offering potential therapeutic targets for ICH treatment. Our study reveals pronounced ferroptosis in microglia and identifies the target gene, cathepsin B (Ctsb), by analyzing differentially expressed genes following ICH. Ctsb, a cysteine protease primarily located in lysosomes, becomes a focal point in our investigation. Utilizing in vitro and in vivo models, we explore the correlation between Ctsb and ferroptosis in microglia post-ICH. Results demonstrate that ICH and hemin-induced ferroptosis in microglia coincide with elevated levels and activity of Ctsb protein. Effective alleviation of ferroptosis in microglia after ICH is achieved through the inhibition of Ctsb protease activity and protein levels using inhibitors and shRNA. Additionally, a notable increase in m6A methylation levels of Ctsb mRNA post-ICH is observed, suggesting a pivotal role of m6A methylation in regulating Ctsb translation. These research insights deepen our comprehension of the molecular pathways involved in ferroptosis after ICH, underscoring the potential of Ctsb as a promising target for mitigating brain damage resulting from ICH.