Photochemical dearomative skeletal modifications of heteroaromatics.

Dearomatization has emerged as a powerful tool for rapid construction of 3D molecular architectures from simple, abundant, and planar (hetero)arenes. The field has evolved beyond simple dearomatization driven by new synthetic technology development. With the renaissance of photocatalysis and expansion of the activation mode, the last few years have witnessed impressive developments in innovative photochemical dearomatization methodologies, enabling skeletal modifications of dearomatized structures. They offer truly efficient and useful tools for facile construction of highly complex structures, which are viable for natural product synthesis and drug discovery. In this review, we aim to provide a mechanistically insightful overview on these innovations based on the degree of skeletal alteration, categorized into dearomative functionalization and skeletal editing, and to highlight their synthetic utilities.

Reversing Metabolic Dysfunction-Associated Steatotic Liver Disease Promotes Healthy Pediatric Cardiovascular Structures: The PROC Study.

INTRODUCTION: We investigated the impact of metabolic dysfunction-associated steatotic liver disease (MASLD) on cardiovascular structure development in children. METHODS: We followed 1,356 children with the mean age of 6.6 years for 4.5 years in Beijing, China. We assessed the association of MASLD with cardiovascular structure (carotid intima-media thickness and left ventricular mass) outcomes at baseline and follow-up. RESULTS: Over follow-up, 59 children had persistent MASLD, 109 had incident MASLD (progression), and 35 had normalization of liver health. Children with MASLD normalization showed a significantly lower mean development in carotid intima-media thickness (0.161 vs 0.188 mm) and left ventricular mass (4.5 vs 12.4 g) than children with persistent MASLD. DISCUSSION: The control of MASLD was associated with improved cardiovascular structure development.

Multi-model predictive analysis of RNA solvent accessibility based on modified residual attention mechanism.

Predicting RNA solvent accessibility using only primary sequence data can be regarded as sequence-based prediction work. Currently, the established studies for sequence-based RNA solvent accessibility prediction are limited due to the available number of datasets and black box prediction. To improve these issues, we first expanded the available RNA structures and then developed a sequence-based model using modified attention layers with different receptive fields to conform to the stem-loop structure of RNA chains. We measured the improvement with an extended dataset and further explored the model's interpretability by analysing the model structures, attention values and hyperparameters. Finally, we found that the developed model regarded the pieces of a sequence as templates during the training process. This work will be helpful for researchers who would like to build RNA attribute prediction models using deep learning in the future.

Geraniol attenuates oxygen-glucose deprivation/reoxygenation-induced ROS-dependent apoptosis and permeability of human brain microvascular endothelial cells by activating the Nrf-2/HO-1 pathway.

Blood-brain barrier breakdown and ROS overproduction are important events during the progression of ischemic stroke aggravating brain damage. Geraniol, a natural monoterpenoid, possesses anti-apoptotic, cytoprotective, anti-oxidant, and anti-inflammatory activities. Our study aimed to investigate the effect and underlying mechanisms of geraniol in oxygen-glucose deprivation/reoxygenation (OGD/R)-induced human brain microvascular endothelial cells (HBMECs). Apoptosis, caspase-3 activity, and cytotoxicity of HBMECs were evaluated using TUNEL, caspase-3 activity, and CCK-8 assays, respectively. The permeability of HBMECs was examined using FITC-dextran assay. Reactive oxygen species (ROS) production was measured using the fluorescent probe DCFH-DA. The protein levels of zonula occludens-1 (ZO-1), occludin, claudin-5, ß-catenin, nuclear factor erythroid 2-related factor 2 (Nrf2), and heme oxygenase-1 (HO-1) were determined by western blotting. Geraniol showed no cytotoxicity in HBMECs. Geraniol and ROS scavenger N-acetylcysteine (NAC) both attenuated OGD/R-induced apoptosis and increase of caspase-3 activity and the permeability to FITC-dextran in HBMECs. Geraniol relieved OGD/R-induced ROS accumulation and decrease of expression of ZO-1, occludin, claudin-5, and ß-catenin in HBMECs. Furthermore, we found that geraniol activated Nrf2/HO-1 pathway to inhibit ROS in HBMECs. In conclusion, geraniol attenuated OGD/R-induced ROS-dependent apoptosis and permeability in HBMECs through activating the Nrf2/HO-1 pathway.

Attention-based deep clustering method for scRNA-seq cell type identification.

Single-cell sequencing (scRNA-seq) technology provides higher resolution of cellular differences than bulk RNA sequencing and reveals the heterogeneity in biological research. The analysis of scRNA-seq datasets is premised on the subpopulation assignment. When an appropriate reference is not available, such as specific marker genes and single-cell reference atlas, unsupervised clustering approaches become the predominant option. However, the inherent sparsity and high-dimensionality of scRNA-seq datasets pose specific analytical challenges to traditional clustering methods. Therefore, a various deep learning-based methods have been proposed to address these challenges. As each method improves partially, a comprehensive method needs to be proposed. In this article, we propose a novel scRNA-seq data clustering method named AttentionAE-sc (Attention fusion AutoEncoder for single-cell). Two different scRNA-seq clustering strategies are combined through an attention mechanism, that include zero-inflated negative binomial (ZINB)-based methods dealing with the impact of dropout events and graph autoencoder (GAE)-based methods relying on information from neighbors to guide the dimension reduction. Based on an iterative fusion between denoising and topological embeddings, AttentionAE-sc can easily acquire clustering-friendly cell representations that similar cells are closer in the hidden embedding. Compared with several state-of-art baseline methods, AttentionAE-sc demonstrated excellent clustering performance on 16 real scRNA-seq datasets without the need to specify the number of groups. Additionally, AttentionAE-sc learned improved cell representations and exhibited enhanced stability and robustness. Furthermore, AttentionAE-sc achieved remarkable identification in a breast cancer single-cell atlas dataset and provided valuable insights into the heterogeneity among different cell subtypes.

HOPEXGB: A Consensual Model for Predicting miRNA/lncRNA-Disease Associations Using a Heterogeneous Disease-miRNA-lncRNA Information Network.

Predicting disease-related microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) is crucial to find new biomarkers for the prevention, diagnosis, and treatment of complex human diseases. Computational predictions for miRNA/lncRNA-disease associations are of great practical significance, since traditional experimental detection is expensive and time-consuming. In this paper, we proposed a consensual machine-learning technique-based prediction approach to identify disease-related miRNAs and lncRNAs by high-order proximity preserved embedding (HOPE) and eXtreme Gradient Boosting (XGB), named HOPEXGB. By connecting lncRNA, miRNA, and disease nodes based on their correlations and relationships, we first created a heterogeneous disease-miRNA-lncRNA (DML) information network to achieve an effective fusion of information on similarities, correlations, and interactions among miRNAs, lncRNAs, and diseases. In addition, a more rational negative data set was generated based on the similarities of unknown associations with the known ones, so as to effectively reduce the false negative rate in the data set for model construction. By 10-fold cross-validation, HOPE shows better performance than other graph embedding methods. The final consensual HOPEXGB model yields robust performance with a mean prediction accuracy of 0.9569 and also demonstrates high sensitivity and specificity advantages compared to lncRNA/miRNA-specific predictions. Moreover, it is superior to other existing methods and gives promising performance on the external testing data, indicating that integrating the information on lncRNA-miRNA interactions and the similarities of lncRNAs/miRNAs is beneficial for improving the prediction performance of the model. Finally, case studies on lung, stomach, and breast cancers indicate that HOPEXGB could be a powerful tool for preclinical biomarker detection and bioexperiment preliminary screening for the diagnosis and prognosis of cancers. HOPEXGB is publicly available at https://github.com/airpamper/HOPEXGB.

Advancing Adverse Drug Reaction Prediction with Deep Chemical Language Model for Drug Safety Evaluation.

The accurate prediction of adverse drug reactions (ADRs) is essential for comprehensive drug safety evaluation. Pre-trained deep chemical language models have emerged as powerful tools capable of automatically learning molecular structural features from large-scale datasets, showing promising capabilities for the downstream prediction of molecular properties. However, the performance of pre-trained chemical language models in predicting ADRs, especially idiosyncratic ADRs induced by marketed drugs, remains largely unexplored. In this study, we propose MoLFormer-XL, a pre-trained model for encoding molecular features from canonical SMILES, in conjunction with a CNN-based model to predict drug-induced QT interval prolongation (DIQT), drug-induced teratogenicity (DIT), and drug-induced rhabdomyolysis (DIR). Our results demonstrate that the proposed model outperforms conventional models applied in previous studies for predicting DIQT, DIT, and DIR. Notably, an analysis of the learned linear attention maps highlights amines, alcohol, ethers, and aromatic halogen compounds as strongly associated with the three types of ADRs. These findings hold promise for enhancing drug discovery pipelines and reducing the drug attrition rate due to safety concerns.

Attention Mechanism-Based Graph Neural Network Model for Effective Activity Prediction of SARS-CoV-2 Main Protease Inhibitors: Application to Drug Repurposing as Potential COVID-19 Therapy.

Compared to de novo drug discovery, drug repurposing provides a time-efficient way to treat coronavirus disease 19 (COVID-19) that is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). SARS-CoV-2 main protease (Mpro) has been proved to be an attractive drug target due to its pivotal involvement in viral replication and transcription. Here, we present a graph neural network-based deep-learning (DL) strategy to prioritize the existing drugs for their potential therapeutic effects against SARS-CoV-2 Mpro. Mpro inhibitors were represented as molecular graphs ready for graph attention network (GAT) and graph isomorphism network (GIN) modeling for predicting the inhibitory activities. The result shows that the GAT model outperforms the GIN and other competitive models and yields satisfactory predictions for unseen Mpro inhibitors, confirming its robustness and generalization. The attention mechanism of GAT enables to capture the dominant substructures and thus to realize the interpretability of the model. Finally, we applied the optimal GAT model in conjunction with molecular docking simulations to screen the Drug Repurposing Hub (DRH) database. As a result, 18 drug hits with best consensus prediction scores and binding affinity values were identified as the potential therapeutics against COVID-19. Both the extensive literature searching and evaluations on adsorption, distribution, metabolism, excretion, and toxicity (ADMET) illustrate the premium drug-likeness and pharmacokinetic properties of the drug candidates. Overall, our work not only provides an effective GAT-based DL prediction tool for inhibitory activity of SARS-CoV-2 Mpro inhibitors but also provides theoretical guidelines for drug discovery in the COVID-19 treatment.

Target discovery of bioactive natural products with native-compound-coupled CNBr-activated Sepharose 4B beads (NCCB): Applications, mechanisms and outlooks.

Natural products (NPs) represent a treasure trove for drug discovery and development due to their chemical structural diversity and a broad spectrum of biological activities. Uncovering the biological targets and understanding their molecular mechanism of actions are crucial steps in the development of clinical therapeutics. However, the structural complexity of NPs and intricate nature of biological system present formidable challenges in target identification of NPs. Although significant advances have been made in the development of new chemical tools, these methods often require high levels of synthetic skills for preparing chemical probes. This can be costly and time-consuming relaying on operationally complicated procedures and instruments. In recent efforts, we and others have successfully developed an operationally simple and practical chemical tool known as native-compound-coupled CNBr-activated Sepharose 4B beads (NCCB) for NP target identification. In this approach, a native compound readily reacts with commercial CNBr-activated Sepharose 4B beads with a process that is easily performed in any biology laboratory. Based on NCCB, our group has identified the direct targets of more than 60 NPs. In this review, we will elucidate the application scopes, including flavonoids, quinones, terpenoids and others, characteristics, chemical mechanisms, procedures, advantages, disadvantages, and future directions of NCCB in specific target discovery.

Native-compound-Coupled Affinity Matrix (NCAM) in target identification and validation of bioactive compounds: Application, mechanism and outlooks.

In drug discovery and development, the direct target identification of bioactive small molecules plays a significant role for understanding the mechanism of action, predicting the side effects, and rationally designing more potent compounds. However, due to the complicated regulatory processes in a cell together with thousands of biomacromolecules, target identification is always the major obstacle. New methods and technologies are continuously invented to tackle this problem. Nevertheless, the mainly used tools possess several disadvantages. High synthetic skills are typically required to laboriously synthesize a probe for protein enrichment. To detect the ligand-protein interaction by analyzing proteins' responses to proteolytic or thermal treatment, costly and precise instruments are always necessary. Therefore, convenient and practical techniques are urgently needed. Over the past decades, a strategy using native compounds without the requirement of chemical modification, also termed Native-compound-Coupled Affinity Matrix (NCAM), is developing continuously. Two practical tactics based on "label-free" compounds have been invented and used, that is Photo-cross-linked Small-molecule Affinity Matrix (PSAM) and Native-compound-Coupled CNBr-activated Beads (NCCB). Presently, we will elucidate the characteristics, coupling mechanism, advantages and disadvantages, and future prospect of NCAM in specific target identification and validation.

A consensual machine-learning-assisted QSAR model for effective bioactivity prediction of xanthine oxidase inhibitors using molecular fingerprints.

Xanthine oxidase inhibitors (XOIs) have been widely studied due to the promising potential as safe and effective therapeutics in hyperuricemia and gout. Currently, available XOI molecules have been developed from different experiments but they are with the wide structure diversity and significant varying bioactivities. So it is of great practical significance to present a consensual QSAR model for effective bioactivity prediction of XOIs based on a systematic compiling of these XOIs across different experiments. In this work, 249 XOIs belonging to 16 scaffolds were collected and were integrated into a consensual dataset by introducing the concept of IC50 values relative to allopurinol (RIC50). Here, extended connectivity fingerprints (ECFPs) were employed to represent XOI molecules. By performing effective feature selection by machine-learning method, 54 crucial fingerprints were indicated to be valuable for predicting the inhibitory potency (IP) of XOIs. The optimal predictor yields the promising performance by different cross-validation tests. Besides, an external validation of 43 XOIs and a case study on febuxostat also provide satisfactory results, indicating the powerful generalization of our predictor. Here, the predictor was interpreted by shapely additive explanation (SHAP) method which revealed several important substructures by mapping the featured fingerprints to molecular structures. Then, 15 new molecules were designed and predicted by our predictor to show superior IP than febuxostat. Finally, molecular docking simulation was performed to gain a deep insight into molecular binding mode with xanthine oxidase (XO) enzyme, showing that molecules with selenazole moiety, cyano group and isopropyl group tended to yield higher IP. The absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction results further enhanced the potential of these novel XOIs as drug candidates. Overall, this work presents a QSAR model for accurate prediction of IP of XOIs, and is expected to provide new insights for further structure-guided design of novel XOIs.

A pilot study on identifying gene signatures as markers for predicting patient response to antiseizure medications.

The majority of the biomarkers were associated with the diagnosis of epilepsy and few of them can be applied to predict the response to antiseizure medications (ASMs). In this study, we identified 26 significantly up-regulated genes and 32 down-regulated genes by comparing the gene expression profiles of patients with epilepsy that responded to valproate with those without applying any ASM. The results of gene set enrichment analysis indicated that the ferroptosis pathway was significantly impacted (p = 0.0087) in patients who responded to valproate. Interestingly, the gene NCOA4 in this pathway exhibited significantly different expression levels between the two groups, indicating that NCOA4 could serve as a potential biomarker to better understand the mechanism of valproate resistance. In addition, six up-regulated genes SF3A2, HMGN2, PABPN1, SSBP3, EFTUD2, and CREB3L2 as well as six down-regulated genes ZFP36L1, ACRC, SUB1, CALM2, TLK1, and STX2 also showed significantly different expression patterns between the two groups. Moreover, based on the gene expression profiles of the patients with the treatment of valproate, carbamazepine, and phenytoin, we proposed a strategy for predicting the response to the ASMs by using the Connectivity Map scoring method. Our findings could be helpful for better understanding the mechanisms of drug resistance of ASMs and improving the clinical treatment of epilepsy.

The association between nasal mucosa bacteria and serum metals in children with nasal diseases.

Allergic rhinitis (AR) and adenoid hypertrophy (AH) are common nasal diseases in children. Studies have shown that heavy metals are environmental risk factors for nasal diseases, and the pathogenic mechanisms may be related to dysregulation of nasal mucosal microbiota. However, it is unclear how heavy metal exposure relates to the nasal mucosal microbiota in nasal diseases. Therefore, we explored serum metal exposure levels and nasal mucosal microbiota composition in children with different nasal disease, and further studied the potential correlation between metal exposure and disease-related taxa. There were 64 children recruited for this study. The 23 metals concentrations in serum were measured by inductively coupled plasma mass spectrometry, and nasal mucosal bacteria was identified by 16S rRNA sequencing. Nasal diseases (AR and AH) in children were associated with alterations in the abundance and diversity of the nasal mucosal microbiota. The nasal microbiota of children with AR showed lower diversity, while the microbiota of children with AH showed higher diversity. Linear discriminant analysis Effect Size showed 108 differentially abundant taxa between AR and control groups, 35 differentially abundant taxa among large adenoid, moderate adenoid and small adenoid groups. The serum zinc concentration was negatively correlated with Pielou's eveness index and Simpson's Index in children classified by adenoid size. The spearman correlation analysis showed that multiple disease-related taxa were closely associated with metal concentrations in serum. Our findings may support a link between metal exposure and the diversity and composition of nasal bacteria in children with nasal disease, which present new evidence for the effects of metals on children health.

Unraveling Structural Alerts in Marketed Drugs for Improving Adverse Outcome Pathway Framework of Drug-Induced QT Prolongation.

In pharmaceutical treatment, many non-cardiac drugs carry the risk of prolonging the QT interval, which can lead to fatal cardiac complications such as torsades de points (TdP). Although the unexpected blockade of ion channels has been widely considered to be one of the main reasons for affecting the repolarization phase of the cardiac action potential and leading to QT interval prolongation, the lack of knowledge regarding chemical structures in drugs that may induce the prolongation of the QT interval remains a barrier to further understanding the underlying mechanism and developing an effective prediction strategy. In this study, we thoroughly investigated the differences in chemical structures between QT-prolonging drugs and drugs with no drug-induced QT prolongation (DIQT) concerns, based on the Drug-Induced QT Prolongation Atlas (DIQTA) dataset. Three categories of structural alerts (SAs), namely amines, ethers, and aromatic compounds, appeared in large quantities in QT-prolonging drugs, but rarely in drugs with no DIQT concerns, indicating a close association between SAs and the risk of DIQT. Moreover, using the molecular descriptors associated with these three categories of SAs as features, the structure-activity relationship (SAR) model for predicting the high risk of inducing QT interval prolongation of marketed drugs achieved recall rates of 72.5% and 80.0% for the DIQTA dataset and the FDA Adverse Event Reporting System (FAERS) dataset, respectively. Our findings may promote a better understanding of the mechanism of DIQT and facilitate research on cardiac adverse drug reactions in drug development.

Association between serum levels of TSH and free T4 and per- and polyfluoroalkyl compounds concentrations in pregnant women.

Many per- and polyfluoralkyl substances (PFASs) may disrupt maternal thyroid hormone homeostasis in pregnancy. Concerns should be raised regarding the PFASs exposure in pregnant women because thyroid hormones are involved in the early development of the fetus. In this study, we measured the concentrations of 13 PFASs, including five novel short-chain PFASs, in serum from 123 pregnant women in Beijing, China. Linear regression models were used to investigate the association between thyroid-stimulating hormone (TSH) or free thyroxine (FT4) levels and PFASs concentrations under consideration of the impacts of pregnancy-induced physiological factors. We found that perfluorobutanoic acid (PFBA) (ß=0.189, 95%CI=-0.039, 0.417, p=0.10) and perfluorodecanoic acid (PFDA) (ß=-0.554, 95%CI=-1.16, 0.049, p=0.071) were suggestive of significant association with TSH in thyroid peroxidase antibody (TPOAb) negative women. No association was observed between all PFASs and FT4 levels after controlling for these confounding factors, such as BMI, gestational weight gain and maternal age. These findings suggest that it should pay more attention to the association between thyroid hormone levels and short-chain PFASs concentrations. Future studies could consider a greater sample and the inclusion of other clinical indicators of thyroid function, such as free T3 and total T3.

The landscape of miRNA-related ceRNA networks for marking different renal cell carcinoma subtypes.

We know that different types of cancers usually have different responses to the same treatment. Therefore, it is important to understand the similarities and differences across subtypes of cancers, so as to provide a basis for the individualized treatments. Until now, no comprehensive investigation on competing endogenous RNAs (ceRNAs) has been reported for the three main subtypes of renal cell carcinoma (RCC), so the regulation characteristics of ceRNAs in three subtypes are not well revealed. This paper firstly describes a comparative analysis of ceRNA-ceRNA interaction networks for all the three subtypes of RCC based on differential microRNAs (miRNAs). We comprehensively summarized all miRNA and messenger RNAdata of RCC from 126 matched tumor-normal tissues in The Cancer Genome Atlas, systematically analyzed a total of more than 80 000 ceRNA interactions and highlighted the common and specific properties among them, aiming to identify critical genes to classify them for providing supplementary help in the precise diagnosis of RCC. From three aspects, including common or specific ceRNAs, upregulated or downregulated and classifications across the three subtypes, we highlighted the common and specific properties for the three subtypes and also explored the classification of RCC by combining the specific ceRNAs with differential regulations. Moreover, for the most major subtype of clear cell renal cell carcinoma (KIRC), three critical genes were screened out from KIRC ceRNA network and further demonstrated to be the potential biomarkers of KIRC by performing biological experiments at the transcriptional level.

Predictors of non-alcoholic fatty liver disease in children.

OBJECTIVE: Abdominal obesity is strongly associated with the development of non-alcoholic fatty liver disease (NAFLD). Early identification and intervention may reduce the risk. We aim to improve pediatric NAFLD screening by comparing discriminative performance of six abdominal obesity indicators. METHODS: We measured anthropometric indicators (waist circumference [WC], waist-to-hip ratio [WHR], waist-to-height ratio [WHtR]), body composition indicators (trunk fat index [TFI], visceral fat area [VFA]), and endocrine indicator (visceral adiposity index [VAI]) among 1350 Chinese children aged 6-8 years. Using Spearman correlation, receiver operating characteristic (ROC) curves, and Logistic regression, we validated their ability to predict NAFLD. RESULTS: All six indicators can predict NAFLD robustly, with area under the curve (AUC) values ranged from 0.69 to 0.96. TFI, WC, and VFA rank in the top three for the discriminative performance. TFI was the best predictor with AUC values of 0.94 (0.92-0.97) and 0.96 (0.92-0.99), corresponding to cut-off values of 1.83 and 2.31 kg/m2 for boys and girls, respectively. Boys with higher TFI (aOR = 13.8), VFA (aOR = 11.1), WHtR (aOR = 3.1), or VAI (aOR = 2.8), and girls with higher TFI (aOR = 21.0) or VFA (aOR = 17.5), were more likely to have NAFLD. CONCLUSION: User-friendly body composition indicators like TFI can identify NAFLD and help prevent the progress of liver disease. TRIAL REGISTRATION: Chinese Clinical Trial Registry (ChiCTR) ( www.chictr.org.cn/enIndex.aspx , No. ChiCTR2100044027); retrospectively registered on 6 March 2021. IMPACT: Abdominal obesity increases the risk of pediatric non-alcoholic fatty liver disease (NAFLD). This study compared the discriminative performance of multiple abdominal obesity indicators measured by different methods in terms of accuracy and fastidious cut-off values through a population-based child cohort. Our results provided solid evidence of abdominal obesity indicators as an optimal screening tool for pediatric NAFLD, with area under the curve (AUC) values ranged from 0.69 to 0.96. User-friendly body composition indicators like TFI show a greater application potential in helping physicians perform easy, reliable, and interpretable weight management to prevent the progress of liver damage.

COVID-19 Clinical Presentation Among HIV-Infected Persons in China: A Systematic Review.

PURPOSE OF REVIEW: The impact of HIV infection on the natural history of COVID-19 is unknown, given the recency of the human spread of SARS-CoV-2 (CoV). We reviewed published case series/reports of CoV-HIV coinfections to clarify epidemiologic and clinical features in China, the first nation with pandemic experience. RECENT FINDINGS: Assuming that HIV-infected persons were at average risk of CoV infection in Wuhan, we estimated HIV-CoV coinfected persons to number 412 (95%CI: 381-442); our review encompassed an estimated 16.7% (69/412) of Wuhan. Men (many of whom reported sex with other men) accounted for 71.1% (54/76) of the cases reported in China. The median age was 48.0 years old (range 24-77, interquartile:37-57). The median CD4+ cell count at the last clinical visit was 421 cells/µL; 83.0% had an undetectable viral load. Among 31 patients with clinical details reported, fatigue (41.9%), respiratory distress (41.9%), and gastrointestinal symptoms (26.7%) were most common. Among the 52 cases reporting COVID-19 clinical severity, 46.2% were severe, 44.2% mild, and 9.6% asymptomatic COVID-19. Late antiretroviral therapy (ART) was reported by 30.4% (7/23) among whom 57.1% (4/7) were confirmed as severe COVID-19. The case fatality rate was 9.1% (3/33). Severe disease and death were less common among persons who took ART prior to the COVID-19 diagnosis. Of 16 reported IL-6 results, 68.7% were within the normal range. Earlier use of ART was associated with a better COVID-19 prognosis with CoV-HIV co-infection reported from China through early 2021, but small sample sizes limit definitive conclusions.

Passive Smoking Exposure Modifies Cardiovascular Structure and Function: Beijing Child Growth and Health Cohort (PROC) Study.

Passive smoking exposure in children is prevalent worldwide and exposes children to respiratory and systemic toxins. In this study, we enrolled 568 children to study how secondhand smoke (SHS) might affect children's cardiovascular health in China. The measurement of nicotine and its metabolites in urine showed that 78.9% of children were exposed to SHS. Children exposed to SHS had greater interventricular septum thickness (p = 0.005) and left ventricular mass index (p = 0.008) than nonexposed children. Urinary norcotinine levels were associated with increased ascending aorta diameter (ß = 0.10, 95%CI 0.02-0.17) and decreased left ventricular end systolic diameter (ß = -0.10, 95%CI -0.19 to -0.01). The effects of SHS exposure on cardiovascular function: norcotinine levels associated with lower left ventricular mass index (ß = -0.32, 95%CI -0.59 to -0.05), left ventricular end diastolic volume index (ß = -0.43, 95%CI -0.85 to -0.02), and left ventricular end systolic volume index (ß = -0.20, 95%CI -0.37 to -0.03). Moreover, there no no significant associations of nicotine, cotinine, and trans-3'-hydroxycotinine with cardiovascular health. Overall, SHS exposure in children remains prevalent in Beijing and may affect children's cardiovascular development, in both structure and function. It suggests that stricter and practical measures are needed toward the elimination of tobacco use in children's environments.

A Transfer-Learning-Based Deep Convolutional Neural Network for Predicting Leukemia-Related Phosphorylation Sites from Protein Primary Sequences.

As one of the most important post-translational modifications (PTMs), phosphorylation refers to the binding of a phosphate group with amino acid residues like Ser (S), Thr (T) and Tyr (Y) thus resulting in diverse functions at the molecular level. Abnormal phosphorylation has been proved to be closely related with human diseases. To our knowledge, no research has been reported describing specific disease-associated phosphorylation sites prediction which is of great significance for comprehensive understanding of disease mechanism. In this work, focusing on three types of leukemia, we aim to develop a reliable leukemia-related phosphorylation site prediction models by combing deep convolutional neural network (CNN) with transfer-learning. CNN could automatically discover complex representations of phosphorylation patterns from the raw sequences, and hence it provides a powerful tool for improvement of leukemia-related phosphorylation site prediction. With the largest dataset of myelogenous leukemia, the optimal models for S/T/Y phosphorylation sites give the AUC values of 0.8784, 0.8328 and 0.7716 respectively. When transferred learning on the small size datasets, the models for T-cell and lymphoid leukemia also give the promising performance by common sharing the optimal parameters. Compared with other five machine-learning methods, our CNN models reveal the superior performance. Finally, the leukemia-related pathogenesis analysis and distribution analysis on phosphorylated proteins along with K-means clustering analysis and position-specific conversation profiles on the phosphorylation site all indicate the strong practical feasibility of our easy-to-use CNN models.