GNN-DDAS: Drug discovery for identifying anti-schistosome small molecules based on graph neural network.

Schistosomiasis is a tropical disease that poses a significant risk to hundreds of millions of people, yet often goes unnoticed. While praziquantel, a widely used anti-schistosome drug, has a low cost and a high cure rate, it has several drawbacks. These include ineffectiveness against schistosome larvae, reduced efficacy in young children, and emerging drug resistance. Discovering new and active anti-schistosome small molecules is therefore critical, but this process presents the challenge of low accuracy in computer-aided methods. To address this issue, we proposed GNN-DDAS, a novel deep learning framework based on graph neural networks (GNN), designed for drug discovery to identify active anti-schistosome (DDAS) small molecules. Initially, a multi-layer perceptron was used to derive sequence features from various representations of small molecule SMILES. Next, GNN was employed to extract structural features from molecular graphs. Finally, the extracted sequence and structural features were then concatenated and fed into a fully connected network to predict active anti-schistosome small molecules. Experimental results showed that GNN-DDAS exhibited superior performance compared to the benchmark methods on both benchmark and real-world application datasets. Additionally, the use of GNNExplainer model allowed us to analyze the key substructure features of small molecules, providing insight into the effectiveness of GNN-DDAS. Overall, GNN-DDAS provided a promising solution for discovering new and active anti-schistosome small molecules.

MvGraphDTA: multi-view-based graph deep model for drug-target affinity prediction by introducing the graphs and line graphs.

BACKGROUND: Accurately identifying drug-target affinity (DTA) plays a pivotal role in drug screening, design, and repurposing in pharmaceutical industry. It not only reduces the time, labor, and economic costs associated with biological experiments but also expedites drug development process. However, achieving the desired level of computational accuracy for DTA identification methods remains a significant challenge. RESULTS: We proposed a novel multi-view-based graph deep model known as MvGraphDTA for DTA prediction. MvGraphDTA employed a graph convolutional network (GCN) to extract the structural features from original graphs of drugs and targets, respectively. It went a step further by constructing line graphs with edges as vertices based on original graphs of drugs and targets. GCN was also used to extract the relationship features within their line graphs. To enhance the complementarity between the extracted features from original graphs and line graphs, MvGraphDTA fused the extracted multi-view features of drugs and targets, respectively. Finally, these fused features were concatenated and passed through a fully connected (FC) network to predict DTA. CONCLUSIONS: During the experiments, we performed data augmentation on all the training sets used. Experimental results showed that MvGraphDTA outperformed the competitive state-of-the-art methods on benchmark datasets for DTA prediction. Additionally, we evaluated the universality and generalization performance of MvGraphDTA on additional datasets. Experimental outcomes revealed that MvGraphDTA exhibited good universality and generalization capability, making it a reliable tool for drug-target interaction prediction.

Advances and challenges in serine in the central nervous system: physicochemistry, physiology, and pharmacology.

Neurological disorders are the primary cause of human disability and mortality globally, however, current medications slightly alleviate some symptoms of degenerative diseases. Serine is an important amino acid for the brain function and involved in a variety of biosynthetic pathways and signal transduction processes. The imbalance of serine metabolism is associated with neurodegeneration, including neuroinflammation, oxidative stress and apoptosis. Altered activities of serine metabolizing enzymes and accumulation of serine metabolites affect the survival and function of nerve cells. Abnormal serine levels are observed in animal models with neurological diseases, but not all human studies, therefore, the maintenance of serine homeostasis is a potentially therapeutic strategy for neurological disorders. To date, physiological and pharmacological roles of serine in neurological diseases have not been systemically recapitulated, and the association between serine and neurological diseases is controversial. In this review, we summarize physicochemical properties of serine, biological processes of serine in the brain (source, biotransformation, and transport), and the application of serine in neurological diseases including Alzheimer's disease, schizophrenia, and depression. Here, we highlight physicochemistry, physiology, pharmacology, and therapeutic potentials of serine in the prevention and treatment of neurological dysfunction. Our work provides valuable hints for future investigation that will lead to a comprehensive understanding of serine and its metabolism in cellular physiology and pharmacology. Although broad by necessity, the review helps researchers to understand great potentials of serine in the prevention and treatment of neurological dysfunction.

Selenium intake is an effective strategy for the improvement of cognitive decline in low cognition older Americans.

Age-related cognitive decline is a prominent concern in older adults and selenium (Se) deficiency has been found to be associated with cognitive deficits. For the first time, the present study explored the association between Se intake and cognitive performance in older people with/without cognitive impairment using the data from the National Health and Nutrition Examination Survey 2011-2014. Weighted linear regression models were conducted to evaluate the association between dietary Se/total Se intakes and cognitive assessments. A total of 2387 participants were included. The significant positive association between dietary Se/total Se intakes and total scores of cognitive functioning tests existed only in the older people with low cognitive performance (p < 0.001), not in those with normal cognitive performance. In conclusion, Se intake was beneficial for cognitive decline only in the low cognition older people but failed in normal cognition older people.

A global transcriptional activator involved in the iron homeostasis in cyanobacteria.

Cyanobacteria use a series of adaptation strategies and a complicated regulatory network to maintain intracellular iron (Fe) homeostasis. Here, a global activator named IutR has been identified through three-dimensional chromosome organization and transcriptome analysis in a model cyanobacterium Synechocystis sp. PCC 6803. Inactivation of all three homologous IutR-encoding genes resulted in an impaired tolerance of Synechocystis to Fe deficiency and loss of the responses of Fe uptake-related genes to Fe-deplete conditions. Protein-promoter interaction assays confirmed the direct binding of IutR with the promoters of genes related to Fe uptake, and chromatin immunoprecipitation sequencing analysis further revealed that in addition to Fe uptake, IutR could regulate many other physiological processes involved in intracellular Fe homeostasis. These results proved that IutR is an important transcriptional activator, which is essential for cyanobacteria to induce Fe-deficiency response genes. This study provides in-depth insights into the complicated Fe-deficient signaling network and the molecular mechanism of cyanobacteria adaptation to Fe-deficient environments.

New horizons for the role of selenium on cognitive function: advances and challenges.

Cognitive deficits associated with oxidative stress and the dysfunction of the central nervous system are present in some neurodegenerative diseases, such as Alzheimer's disease and Parkinson's disease. Selenium (Se), an essential microelement, exhibits cognition-associated functions through selenoproteins mainly owing to its antioxidant property. Due to the disproportionate distribution of Se in the soil, the amount of Se varies greatly in various foods, resulting in a large proportion of people with Se deficiency worldwide. Numerous cell and animal experiments demonstrate Se deficiency-induced cognitive deficits and Se supplementation-improved cognitive performances. However, human studies yield inconsistent results and the mechanism of Se in cognition still remains elusive, which hinder the further exploration of Se in human cognition. To address the urgent issue, the review summarizes Se-contained foods (plant-based foods, animal-based foods, and Se supplements), brain selenoproteins, mechanisms of Se in cognition (improvement of synaptic plasticity, regulation of Zn2+ level, inhibition of ferroptosis, modulation of autophagy and de novo synthesis of L-serine), and effects of Se on cognitive deficits, as well as consequently sheds light on great potentials of Se in the prevention and treatment of cognitive deficits.

3D printing of interferon γ-preconditioned NSC-derived exosomes/collagen/chitosan biological scaffolds for neurological recovery after TBI.

The reconstruction of neural function and recovery of chronic damage following traumatic brain injury (TBI) remain significant clinical challenges. Exosomes derived from neural stem cells (NSCs) offer various benefits in TBI treatment. Numerous studies confirmed that appropriate preconditioning methods enhanced the targeted efficacy of exosome therapy. Interferon-gamma (IFN-γ) possesses immunomodulatory capabilities and is widely involved in neurological disorders. In this study, IFN-γ was employed for preconditioning NSCs to enhance the efficacy of exosome (IFN-Exo, IE) for TBI. miRNA sequencing revealed the potential of IFN-Exo in promoting neural differentiation and modulating inflammatory responses. Through low-temperature 3D printing, IFN-Exo was combined with collagen/chitosan (3D-CC-IE) to preserve the biological activity of the exosome. The delivery of exosomes via biomaterial scaffolds benefited the retention and therapeutic potential of exosomes, ensuring that they could exert long-term effects at the injury site. The 3D-CC-IE scaffold exhibited excellent biocompatibility and mechanical properties. Subsequently, 3D-CC-IE scaffold significantly improved impaired motor and cognitive functions after TBI in rat. Histological results showed that 3D-CC-IE scaffold markedly facilitated the reconstruction of damaged neural tissue and promoted endogenous neurogenesis. Further mechanistic validation suggested that IFN-Exo alleviated neuroinflammation by modulating the MAPK/mTOR signaling pathway. In summary, the results of this study indicated that 3D-CC-IE scaffold engaged in long-term pathophysiological processes, fostering neural function recovery after TBI, offering a promising regenerative therapy avenue.

Identification of Factors on Blood Selenium Levels in the US Adults: A Cross-Sectional Study.

Selenium (Se) is an essential trace element for humans and its low or high concentration in vivo is associated with the high risk of many diseases. It is important to identify influential factors of Se status. The present study aimed to explore the association between several factors (Se intake, gender, age, race, education, body mass index (BMI), income, smoking and alcohol status) and blood Se concentration using the National Health and Nutrition Examination Survey 2017-2020 data. Demographic characteristics, physical examination, health interviews and diets were compared among quartiles of blood Se concentration using the Rao-Scott χ2 test. Se levels were compared between the different groups of factors studied, measuring the strength of their association. A total of 6205 participants were finally included. The normal reference ranges of blood Se concentration were 142.3 (2.5th percentile) and 240.8 µg/L (97.5th percentile), respectively. The mean values of dietary Se intake, total Se intake and blood Se concentration of the participants were 111.5 µg/day, 122.7 µg/day and 188.7 µg/L, respectively, indicating they were in the normal range. Total Se intake was the most important contributor of blood Se concentration. Gender, race, education status, income, BMI, smoking and alcohol status were associated with blood Se concentration.

Low-intensity ultrasound ameliorates brain organoid integration and rescues microcephaly deficits.

Human brain organoids represent a remarkable platform for modeling neurological disorders and a promising brain repair approach. However, the effects of physical stimulation on their development and integration remain unclear. Here, we report that low-intensity ultrasound significantly increases neural progenitor cell proliferation and neuronal maturation in cortical organoids. Histological assays and single-cell gene expression analyses reveal that low-intensity ultrasound improves the neural development in cortical organoids. Following organoid grafts transplantation into the injured somatosensory cortices of adult mice, longitudinal electrophysiological recordings and histological assays reveal that ultrasound-treated organoid grafts undergo advanced maturation. They also exhibit enhanced pain-related gamma-band activity and more disseminated projections into the host brain than the untreated groups. Finally, low-intensity ultrasound ameliorates neuropathological deficits in a microcephaly brain organoid model. Hence, low-intensity ultrasound stimulation advances the development and integration of brain organoids, providing a strategy for treating neurodevelopmental disorders and repairing cortical damage.

A comprehensive review of the recent advances on predicting drug-target affinity based on deep learning.

Accurate calculation of drug-target affinity (DTA) is crucial for various applications in the pharmaceutical industry, including drug screening, design, and repurposing. However, traditional machine learning methods for calculating DTA often lack accuracy, posing a significant challenge in accurately predicting DTA. Fortunately, deep learning has emerged as a promising approach in computational biology, leading to the development of various deep learning-based methods for DTA prediction. To support researchers in developing novel and highly precision methods, we have provided a comprehensive review of recent advances in predicting DTA using deep learning. We firstly conducted a statistical analysis of commonly used public datasets, providing essential information and introducing the used fields of these datasets. We further explored the common representations of sequences and structures of drugs and targets. These analyses served as the foundation for constructing DTA prediction methods based on deep learning. Next, we focused on explaining how deep learning models, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformer, and Graph Neural Networks (GNNs), were effectively employed in specific DTA prediction methods. We highlighted the unique advantages and applications of these models in the context of DTA prediction. Finally, we conducted a performance analysis of multiple state-of-the-art methods for predicting DTA based on deep learning. The comprehensive review aimed to help researchers understand the shortcomings and advantages of existing methods, and further develop high-precision DTA prediction tool to promote the development of drug discovery.

Systemic immune-inflammation index and all-cause and cause-specific mortality in sarcopenia: a study from National Health and Nutrition Examination Survey 1999-2018.

Background: Sarcopenia, common in the elderly, often linked to chronic diseases, correlates with inflammation.The association between SII and mortality in sarcopenia patients is underexplored, this study investigates this relationship in a U.S. adult cohort. Methods: We analyzed 1999-2018 NHANES data, focusing on 2,974 adults with sarcopenia. Mortality outcomes were determined by linking to National Death Index (NDI) records up to December 31, 2019. Using a weighted sampling design, participants were grouped into three groups by the Systemic Immune-Inflammation Index (SII). We used Cox regression models, adjusting for demographic and clinical variables, to explore SII's association with all-cause and cause-specific mortality in sarcopenia, performing sensitivity analyses for robustness. Results: Over a median follow-up of 9.2 years, 829 deaths occurred. Kaplan-Meier analysis showed significant survival differences across SII groups. The highest SII group showed higher hazard ratios (HRs) for all-cause and cause-specific mortality in both crude and adjusted models. The highest SII group had a higher HR for all-cause(1.57, 1.25-1.98), cardiovascular(1.61, 1.00-2.58), cancer(2.13, 1.32-3.44), and respiratory disease mortality(3.21, 1.66-6.19) in fully adjusted models. Subgroup analyses revealed SII's association with all-cause mortality across various demographics, including age, gender, and presence of diabetes or cardiovascular disease. Sensitivity analyses, excluding participants with cardiovascular diseases, those who died within two years of follow-up, or those under 45 years of age, largely reflected these results, with the highest SII group consistently demonstrating higher HRs for all types of mortality in both unadjusted and adjusted models. Conclusion: Our study is the first to demonstrate a significant relationship between SII and increased mortality risks in a sarcopenia population.

Drug-Online: an online platform for drug-target interaction, affinity, and binding sites identification using deep learning.

BACKGROUND: Accurately identifying drug-target interaction (DTI), affinity (DTA), and binding sites (DTS) is crucial for drug screening, repositioning, and design, as well as for understanding the functions of target. Although there are a few online platforms based on deep learning for drug-target interaction, affinity, and binding sites identification, there is currently no integrated online platforms for all three aspects. RESULTS: Our solution, the novel integrated online platform Drug-Online, has been developed to facilitate drug screening, target identification, and understanding the functions of target in a progressive manner of "interaction-affinity-binding sites". Drug-Online platform consists of three parts: the first part uses the drug-target interaction identification method MGraphDTA, based on graph neural networks (GNN) and convolutional neural networks (CNN), to identify whether there is a drug-target interaction. If an interaction is identified, the second part employs the drug-target affinity identification method MMDTA, also based on GNN and CNN, to calculate the strength of drug-target interaction, i.e., affinity. Finally, the third part identifies drug-target binding sites, i.e., pockets. The method pt-lm-gnn used in this part is also based on GNN. CONCLUSIONS: Drug-Online is a reliable online platform that integrates drug-target interaction, affinity, and binding sites identification. It is freely available via the Internet at http://39.106.7.26:8000/Drug-Online/ .

Enhancing Vault Prediction and ICL Sizing Through Advanced Machine Learning Models.

PURPOSE: To use artificial intelligence (AI) technology to accurately predict vault and Implantable Collamer Lens (ICL) size. METHODS: The methodology focused on enhancing predictive capabilities through the fusion of machine-learning algorithms. Specifically, AdaBoost, Random Forest, Decision Tree, Support Vector Regression, LightGBM, and XGBoost were integrated into a majority-vote model. The performance of each model was evaluated using appropriate metrics such as accuracy, precision, F1-score, and area under the curve (AUC). RESULTS: The majority-vote model exhibited the highest performance among the classification models, with an accuracy of 81.9% area under the curve (AUC) of 0.807. Notably, LightGBM (accuracy = 0.788, AUC = 0.803) and XGBoost (ACC = 0.790, AUC = 0.801) demonstrated competitive results. For the ICL size prediction, the Random Forest model achieved an impressive accuracy of 85.3% (AUC = 0.973), whereas XG-Boost (accuracy = 0.834, AUC = 0.961) and LightGBM (accuracy = 0.816, AUC = 0.961) maintained their compatibility. CONCLUSIONS: This study highlights the potential of diverse machine learning algorithms to enhance postoperative vault and ICL size prediction, ultimately contributing to the safety of ICL implantation procedures. Furthermore, the introduction of the novel majority-vote model demonstrates its capability to combine the advantages of multiple models, yielding superior accuracy. Importantly, this study will empower ophthalmologists to use a precise tool for vault prediction, facilitating informed ICL size selection in clinical practice. [J Refract Surg. 2024;40(3):e126-e132.].

Analysis on the impact of smart city construction on urban greenness in China's megacities.

Urban greenness serves as a key indicator of sustainable urban development, with smart city construction emerging as a primary strategy for its enhancement. However, there is little empirical evidence considering multi-dimension between urban greenness and smart city construction on the city level. This study focuses on the impact on urban greenness of smart city construction in megacities, using the difference-in-differences regression model to evaluate the impact based on urban development conditions in various aspects from 2010 to 2021 in 10 megacities in China. The results of panel data of different indicator samples show unique conclusions. First, smart city pilot policy in megacities has significant impact on urban greenness, primarily due to demographic and economic developments. Second, the impact is different between the megacity and national level, and different factors of urban greenness have different effects on smart city construction. Third, the effects are time-lagged and lasted for years, and regional heterogeneity divided by building climate zones is existed, where the effect is more obvious in city agglomeration. These findings of smart city construction reveal the unique influences on megacity greenness, and can be generalized to cities with similar characteristics accordingly.

Predictability comparison of sizing parameters for postoperative vault after implantable Collamer lens implantation.

PURPOSE: This study aims to assess the accuracy of three parameters (white-to-white distance [WTW], angle-to-angle [ATA], and sulcus-to-sulcus [STS]) in predicting postoperative vault and to formulate an optimized predictive model. METHODS: In this retrospective study, a cohort of 465 patients (comprising 769 eyes) who underwent the implantation of the V4c implantable Collamer lens with a central port (ICL) for myopia correction was examined. Least absolute shrinkage and selection operator (LASSO) regression and classification models were used to predict postoperative vault. The influences of WTW, ATA, and STS on predicting the postoperative vault and ICL size were analyzed and compared. RESULTS: The dataset was randomly divided into training (80%) and test (20%) sets, with no significant differences observed between them. The screened variables included only seven variables which conferred the largest signal in the model, namely, lens thickness (LT, estimated coefficients for logistic least absolute shrinkage of -0.20), STS (-0.04), size (0.08), flat K (-0.006), anterior chamber depth (0.15), spherical error (-0.006), and cylindrical error (-0.0008). The optimal prediction model depended on STS (R2=0.419, RMSE=0.139), whereas the least effective prediction model relied on WTW (R2=0.395, RMSE=0.142). In the classified prediction models of the vault, classification prediction of the vault based on STS exhibited superior accuracy compared to ATA or WTW. CONCLUSIONS: This study compared the capabilities of WTW, ATA, and STS in predicting postoperative vault, demonstrating that STS exhibits a stronger correlation than the other two parameters.

Functional analysis of UDP-glycosyltransferase genes conferring indoxacarb resistance in Spodoptera litura.

UDP-glycosyltransferase (UGT) is the major detoxification enzymes of phase II involved in xenobiotics metabolism, which potentially mediates the formation of insect resistance. Previous transcriptome sequencing studies have found that several UGT genes were upregulated in indoxacarb resistant strains of Spodoptera litura, but whether these UGT genes were involved in indoxacarb resistance and their functions in resistance were unclear. In this study, the UGTs inhibitor, 5-nitrouracil, enhanced the toxicity of indoxacarb against S. litura, preliminarily suggesting that UGTs were participated in indoxacarb resistance. Two UGT genes, UGT33J17 and UGT41D10 were upregulated in the resistant strains and could be induced by indoxacarb. Alignment of UGT protein sequences revealed two conserved donor-binding regions with several key residues that interact with catalytic sites and sugar donors. Further molecular modeling and docking analysis indicated that two UGT proteins were able to stably bind indoxacarb and N-decarbomethoxylated metabolite (DCJW). Furthermore, knockdown of UGT33J17 and UGT41D10 decreased viability of Spli-221 cells and enhanced susceptibility of larvae to indoxacarb. Transgenic overexpression of these genes reduced the toxicity of indoxacarb in Drosophila melanogaster. This work revealed that upregulation of UGT genes significantly contributes to indoxacarb resistance in S. litura, and is of great significance for the development of integrated and sustainable management strategies for resistant pests in the field.