End-to-end sequence-structure-function meta-learning predicts genome-wide chemical-protein interactions for dark proteins.

Systematically discovering protein-ligand interactions across the entire human and pathogen genomes is critical in chemical genomics, protein function prediction, drug discovery, and many other areas. However, more than 90% of gene families remain "dark"-i.e., their small-molecule ligands are undiscovered due to experimental limitations or human/historical biases. Existing computational approaches typically fail when the dark protein differs from those with known ligands. To address this challenge, we have developed a deep learning framework, called PortalCG, which consists of four novel components: (i) a 3-dimensional ligand binding site enhanced sequence pre-training strategy to encode the evolutionary links between ligand-binding sites across gene families; (ii) an end-to-end pretraining-fine-tuning strategy to reduce the impact of inaccuracy of predicted structures on function predictions by recognizing the sequence-structure-function paradigm; (iii) a new out-of-cluster meta-learning algorithm that extracts and accumulates information learned from predicting ligands of distinct gene families (meta-data) and applies the meta-data to a dark gene family; and (iv) a stress model selection step, using different gene families in the test data from those in the training and development data sets to facilitate model deployment in a real-world scenario. In extensive and rigorous benchmark experiments, PortalCG considerably outperformed state-of-the-art techniques of machine learning and protein-ligand docking when applied to dark gene families, and demonstrated its generalization power for target identifications and compound screenings under out-of-distribution (OOD) scenarios. Furthermore, in an external validation for the multi-target compound screening, the performance of PortalCG surpassed the rational design from medicinal chemists. Our results also suggest that a differentiable sequence-structure-function deep learning framework, where protein structural information serves as an intermediate layer, could be superior to conventional methodology where predicted protein structures were used for the compound screening. We applied PortalCG to two case studies to exemplify its potential in drug discovery: designing selective dual-antagonists of dopamine receptors for the treatment of opioid use disorder (OUD), and illuminating the understudied human genome for target diseases that do not yet have effective and safe therapeutics. Our results suggested that PortalCG is a viable solution to the OOD problem in exploring understudied regions of protein functional space.

Antibiotics and Pesticides Enhancing the Transfer of Resistomes among Soil-Bayberry-Fruit Fly Food Chain in the Orchard Ecosystem.

While substantial amounts of antibiotics and pesticides are applied to maintain orchard yields, their influence on the dissemination and risk of antibiotic resisitome in the orchard food chain remains poorly understood. In this study, we characterized the bacterial and fungal communities and differentiated both antibiotic resistance genes (ARGs) and virulence factor genes (VFGs) in the soil, Chinese bayberry (matured and fallen), and fruit fly gut, collected from five geographic locations. Our results showed that fruit fly guts and soils exhibit a higher abundance of ARGs and VFGs compared with bayberry fruits. We identified 112 shared ARGs and 75 shared VFGs, with aminoglycoside and adherence factor genes being among the most abundant. The co-occurrence network revealed some shared microbes, such as Bacillus and Candida, as potential hosts of ARGs, highlighting the vector risks for both above- and below-ground parts of the orchard food chain. Notably, the elevated levels of antibiotics and pesticide residues in orchard soils increase ARGs, mobile genetic elements (MGEs), and VFGs in the soil-bayberry-fruit fly food chain. Our study highlighted that agricultural management, including the overuse of antibiotics and pesticides, could be the key factor in accumulating resistomes in the orchard food chain.

Biological Interactions Mediate Soil Functions by Altering Rare Microbial Communities.

Soil microbes, the main driving force of terrestrial biogeochemical cycles, facilitate soil organic matter turnover. However, the influence of the soil fauna on microbial communities remains poorly understood. We investigated soil microbiota dynamics by introducing competition and predation among fauna into two soil ecosystems with different fertilization histories. The interactions significantly affected rare microbial communities including bacteria and fungi. Predation enhanced the abundance of C/N cycle-related genes. Rare microbial communities are important drivers of soil functional gene enrichment. Key rare microbial taxa, including SM1A02, Gammaproteobacteria, and HSB_OF53-F07, were identified. Metabolomics analysis suggested that increased functional gene abundance may be due to specific microbial metabolic activity mediated by soil fauna interactions. Predation had a stronger effect on rare microbes, functional genes, and microbial metabolism compared to competition. Long-term organic fertilizer application increased the soil resistance to animal interactions. These findings provide a comprehensive understanding of microbial community dynamics under soil biological interactions, emphasizing the roles of competition and predation among soil fauna in terrestrial ecosystems.

GLRA2 gene mutations cause high myopia in humans and mice.

BACKGROUND: High myopia (HM) is a leading cause of blindness that has a strong genetic predisposition. However, its genetic and pathogenic mechanisms remain largely unknown. Thus, this study aims to determine the genetic profile of individuals from two large Chinese families with HM and 200 patients with familial/sporadic HM. We also explored the pathogenic mechanism of HM using HEK293 cells and a mouse model. METHODS: The participants underwent genome-wide linkage analysis and exome sequencing. Visual acuity, electroretinogram response, refractive error, optical parameters and retinal rod cell genesis were measured in knockout mice. Immunofluorescent staining, biotin-labelled membrane protein isolation and electrophysiological characterisation were conducted in cells transfected with overexpression plasmids. RESULTS: A novel HM locus on Xp22.2-p11.4 was identified. Variant c.539C>T (p.Pro180Leu) in GLRA2 gene was co-segregated with HM in the two families. Another variant, c.458G>A (p.Arg153Gln), was identified in a sporadic sample. The Glra2 knockout mice showed myopia-related phenotypes, decreased electroretinogram responses and impaired retinal rod cell genesis. Variants c.458G>A and c.539C>T altered the localisation of GlyRα2 on the cell membrane and decreased agonist sensitivity. CONCLUSION: GLRA2 was identified as a novel HM-causing gene. Its variants would cause HM through altered visual experience by impairing photoperception and visual transmission.

DeepREAL: a deep learning powered multi-scale modeling framework for predicting out-of-distribution ligand-induced GPCR activity.

MOTIVATION: Drug discovery has witnessed intensive exploration of predictive modeling of drug-target physical interactions over two decades. However, a critical knowledge gap needs to be filled for correlating drug-target interactions with clinical outcomes: predicting genome-wide receptor activities or function selectivity, especially agonist versus antagonist, induced by novel chemicals. Two major obstacles compound the difficulty on this task: known data of receptor activity is far too scarce to train a robust model in light of genome-scale applications, and real-world applications need to deploy a model on data from various shifted distributions. RESULTS: To address these challenges, we have developed an end-to-end deep learning framework, DeepREAL, for multi-scale modeling of genome-wide ligand-induced receptor activities. DeepREAL utilizes self-supervised learning on tens of millions of protein sequences and pre-trained binary interaction classification to solve the data distribution shift and data scarcity problems. Extensive benchmark studies on G-protein coupled receptors (GPCRs), which simulate real-world scenarios, demonstrate that DeepREAL achieves state-of-the-art performances in out-of-distribution settings. DeepREAL can be extended to other gene families beyond GPCRs. AVAILABILITY AND IMPLEMENTATION: All data used are downloaded from Pfam (Mistry et al., 2020), GLASS (Chan et al., 2015) and IUPHAR/BPS and the data from reference (Sakamuru et al., 2021). Readers are directed to their official website for original data. Code is available on GitHub https://github.com/XieResearchGroup/DeepREAL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Development of an MRI-Based Radiomics-Clinical Model to Diagnose Liver Fibrosis Secondary to Pancreaticobiliary Maljunction in Children.

BACKGROUND: Preoperative diagnosis of liver fibrosis in children with pancreaticobiliary maljunction (PBM) is needed to guide clinical decision-making and improve patient prognosis. PURPOSE: To develop and validate an MR-based radiomics-clinical nomogram for identifying liver fibrosis in children with PBM. STUDY TYPE: Retrospective. POPULATION: A total of 136 patients with PBM from two centers (center A: 111 patients; center B: 25 patients). Cases from center A were randomly divided into training (74 patients) and internal validation (37 patients) sets. Cases from center B were assigned to the external validation set. Liver fibrosis was determined by histopathological examination. FIELD STRENGTH/SEQUENCE: A 3.0 T (two vendors)/T1-weighted imaging and T2-weighted imaging. ASSESSMENT: Clinical factors associated with liver fibrosis were evaluated. A total of 3562 radiomics features were extracted from segmented liver parenchyma. Maximum relevance minimum redundancy and least absolute shrinkage and selection operator were recruited to screen radiomics features. Based on the selected variables, multivariate logistic regression was used to construct the clinical model, radiomics model, and combined model. The combined model was visualized as a nomogram to show the impact of the radiomics signature and key clinical factors on the individual risk of developing liver fibrosis. STATISTICAL TESTS: Mann-Whitney U and chi-squared tests were used to compare clinical factors. P < 0.05 was considered statistically significant in the final models. RESULTS: Two clinical factors and four radiomics features were selected as they were associated with liver fibrosis in the training (AUC, 0.723, 0.927), internal validation (AUC, 0.718, 0.885), and external validation (AUC, 0.737, 0.865) sets. The radiomics-clinical nomogram yielded the best performance in the training (AUC, 0.977), internal validation (AUC, 0.921), and external validation (AUC, 0.878) sets, with good calibration (P > 0.05). DATA CONCLUSION: Our radiomic-based nomogram is a noninvasive, accurate, and preoperative diagnostic tool that is able to detect liver fibrosis in PBM children. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.

S-alkyl Dithiocarbamates Synthesis through Electrochemical Multicomponent Reaction of Thiols, Hydrogen Sulfide, and Isocyanides.

Dithiocarbamates synthesis is extremely important in plenty of biomedical and agrochemical applications, especially fungicide development, but remains a great challenge. In this work, we have successfully developed a multicomponent reaction protocol to convert H2S into S-alkyl dithiocarbamates under constant current conditions. No additional oxidants nor additional catalysts are required, and due to mild conditions, the reactions display a broad substrate scope, including varieties of thiols or disulfides.

Clinical presentations and outcomes of pancreaticobiliary maljunction in different pediatric age groups.

BACKGROUND: Pancreaticobiliary maljunction (PBM) is a congenital defect, with risk of developing various pancreaticobiliary and hepatic complications. The presentations of PBM in children and adults are believed to be different, but studies on PBM children of different age groups are limited. This study was to evaluate clinicopathologic characteristics and outcomes in PBM children of different ages. METHODS: A total of 166 pediatric patients with PBM were reviewed retrospectively. Clinicopathological, imaging, laboratory, surgical, and follow-up data were collected and analyzed. The patients were divided into three age groups, namely, group A (< 1 year, n = 31), group B (1-3 years, n = 63), and group C (> 3 years, n = 72). RESULTS: The major clinical manifestation was jaundice in group A and abdominal pain and vomiting in groups B and C. Acute pancreatitis was more often seen in group C than group A. The length of common channel was significantly longer in group C than group A, while the maximum diameter of common bile duct in group C was smaller than that in group A. Cholangitis and cholecystitis were more commonly performed in groups B and C, while hepatic fibrosis in group A. Whether preoperatively or postoperatively, group C was more likely to have elevated serum amylase, while groups A and B were more likely to present with abnormal liver function indicators, including the increase of aspartate transaminase, alanine transaminase, and gamma-glutamyl transpeptidase. CONCLUSION: Presentation of PBM varies among different pediatric age groups, thus suggesting that targeted management should be carried out according to these differences.

Identification and validation of radiomic features from computed tomography for preoperative classification of neuroblastic tumors in children.

BACKGROUND: To identify radiomic features that can predict the pathological type of neuroblastic tumor in children. METHODS: Data on neuroblastic tumors in 104 children were retrospectively analyzed. There were 14 cases of ganglioneuroma, 24 cases of ganglioneuroblastoma, and 65 cases of neuroblastoma. Stratified sampling was used to randomly allocate the cases into the training and validation sets in a ratio of 3:1. The maximum relevance-minimum redundancy algorithm was used to identify the top 10 of two clinical features and 851 radiomic features in portal venous-phase contrast-enhanced computed tomography images. Least absolute shrinkage and selection operator regression was used to classify tumors in two binary steps: first as ganglioneuroma compared to the other two types, then as ganglioneuroblastoma compared to neuroblastoma. RESULTS: Based on 10 clinical-radiomic features, the classifier identified ganglioneuroma compared to the other two tumor types in the validation dataset with sensitivity of 100.0%, specificity of 81.8%, and an area under the receiver operating characteristic curve (AUC) of 0.875. The classifier identified ganglioneuroblastoma versus neuroblastoma with a sensitivity of 83.3%, a specificity of 87.5%, and an AUC of 0.854. The overall accuracy of the classifier across all three types of tumors was 80.8%. CONCLUSION: Radiomic features can help predict the pathological type of neuroblastic tumors in children.

Using machine learning models to predict the surgical risk of children with pancreaticobiliary maljunction and biliary dilatation.

PURPOSE: To develop machine learning (ML) models to predict the surgical risk of children with pancreaticobiliary maljunction (PBM) and biliary dilatation. METHODS: The subjects of this study were 157 pediatric patients who underwent surgery for PBM with biliary dilatation between January, 2015 and August, 2022. Using preoperative data, four ML models were developed, including logistic regression (LR), random forest (RF), support vector machine classifier (SVC), and extreme gradient boosting (XGBoost). The performance of each model was assessed via the area under the receiver operator characteristic curve (AUC). Model interpretations were generated by Shapley Additive Explanations. A nomogram was used to validate the best-performing model. RESULTS: Sixty-eight patients (43.3%) were classified as the high-risk surgery group. The XGBoost model (AUC = 0.822) outperformed the LR (AUC = 0.798), RF (AUC = 0.802) and SVC (AUC = 0.804) models. In all four models, enhancement of the choledochal cystic wall and an abnormal position of the right hepatic artery were the two most important features. Moreover, the diameter of the choledochal cyst, bile duct variation, and serum amylase were selected as key predictive factors by all four models. CONCLUSIONS: Using preoperative data, the ML models, especially XGBoost, have the potential to predict the surgical risk of children with PBM and biliary dilatation. The nomogram may provide surgeons early warning to avoid intraoperative iatrogenic injury.

Prediction of post-operative acute pancreatitis in children with pancreaticobiliary maljunction using machine learning model.

PURPOSE: This study aimed to develop a prediction model to identify risk factors for post-operative acute pancreatitis (POAP) in children with pancreaticobiliary maljunction (PBM) by pre-operative analysis of patient variables. METHODS: Logistic regression (LR), support vector machine (SVM), and extreme gradient boosting (XGBoost) models were established using the prospectively collected databases of patients with PBM undergoing surgery which was reviewed in the period comprised between August 2015 and August 2022, at the Children's Hospital of Soochow University. Primarily, the area beneath the receiver-operating curves (AUC), accuracy, sensitivity, and specificity were used to evaluate the model performance. The model was finally validated using the nomogram and clinical impact curve. RESULTS: In total, 111 children with PBM met the inclusion criteria, and 21 children suffered POAP. In the validation dataset, LR models showed the highest performance. The risk nomogram and clinical effect curve demonstrated that the LR model was highly predictive. CONCLUSION: The prediction model based on the LR with a nomogram could be used to predict the risk of POAP in patients with PBM. Protein plugs, age, white blood cell count, and common bile duct diameter were the most relevant contributing factors to the models.

Screening of radiation gastrointestinal injury biomarkers in rat plasma by high-coverage targeted lipidomics.

INTRODUCTION: In the event of radiological accidents and cancer radiotherapies in the clinic, the gastrointestinal (GI) system is vulnerable to ionizing radiation and shows GI injury. Accessible biomarkers may provide means to predict, evaluate, and treat GI tissue damage. The current study investigated radiation GI injury biomarkers in rat plasma. MATERIAL AND METHODS: High-coverage targeted lipidomics was employed to profile lipidome perturbations at 72 h after 0, 1, 2, 3, 5, and 8 Gy (60Co γ-rays at 1 Gy/min) total-body irradiation in male rat jejunum. The results were correlated with previous plasma screening outcomes. RESULTS: In total, 93 differential metabolites and 28 linear dose-responsive metabolites were screened in the jejunum. Moreover, 52 lipid species with significant differences both in jejunum and plasma were obtained. Three lipid species with linear dose-response relationship both in jejunum and plasma were put forth, which exhibited good to excellent sensitivity and specificity in triaging different exposure levels. DISCUSSION: The linear dose-effect relationship of lipid metabolites in the jejunum and the triage performance of radiation GI injury biomarkers in plasma were studied for the first time. CONCLUSION: The present study can provide insights into expanded biomarkers of IR-mediated GI injury and minimally invasive assays for evaluation.

Screening of Lipids for Early Triage and Dose Estimation after Acute Radiation Exposure in Rat Plasma Based on Targeted Lipidomics Analysis.

Rapid early triage and dose estimation is vital for limited medical resource allocation and treatment of a large number of the wounded after radiological accidents. Lipidomics has been utilized to delineate biofluid lipid signatures after irradiation. Here, high-coverage targeted lipidomics was employed to screen radiosensitive lipids after 0, 1, 2, 3, 5, and 8 Gy total body irradiation at 4, 24, and 72 h postirradiation in rat plasma. Ultra-performance liquid chromatography-tandem mass spectrometry with a multiple reaction monitoring method was utilized. In total, 416 individual lipids from 18 major classes were quantified and those biomarkers altered in a dose-dependent manner constituted panel A-panel D. Receiver operator characteristic curve analysis using combined lipids showed good to excellent sensitivity and specificity in triaging different radiation exposure levels (area under curve = 0.814-1.000). The equations for dose estimation were established by stepwise regression analysis for three time points. A novel strategy for radiation early triage and dose estimation was first established and validated using panels of lipids. Our study suggests that it is feasible to acquire quantitative lipid biomarker panels using targeted lipidomics platforms for rapid, high-throughput triage, which can provide further insights in developing lipidomics strategies for radiation biodosimetry in humans.

Qianliexin capsule exerts anti-inflammatory activity in chronic non-bacterial prostatitis and benign prostatic hyperplasia via NF-κB and inflammasome.

Qianliexin capsule (QLX) is a standardized traditional Chinese herbal preparation that has long been used to treat chronic non-bacterial prostatitis (CNP) and benign prostatic hyperplasia (BPH). This study investigated the anti-inflammatory activity of QLX in improving lower urinary tract symptoms (LUTS) associated with CNP and BPH. Rat models of CNP and BPH were induced by oestradiol or testosterone (hormonal imbalance) or chemical inflammation (carrageenan). QLX significantly relieved LUTS in CNP and BPH rat model by reducing prostate enlargement, epithelial thickness, pain response time, urine volume and bleeding time, and by improving prostatic blood flow. The expression of the pro-inflammatory cytokines interleukin (IL)-1ß and tumour necrosis factor (TNF)-α, the pro-inflammatory transcription factor nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), and inflammasome components (NLRP3, caspase-1 and ASC) in CNP and BPH tissues was reduced by QLX addition. QLX treatment was followed by reduced cellular malondialdehyde and increased superoxide dismutase, catalase and glutathione peroxidase activity, consistent with antioxidant activity. Increases in Beclin-1 expression and the LC3II/I ratio following QLX treatment indicated that autophagy had been induced. QLX relieved LUTS in CNP and BPH rat models by inhibiting inflammation. The underlying mechanisms included inhibition of inflammasome activation, NF-κB activation, oxidant stress and autophagy.

Electrochemically promoted oxidative α-cyanation of tertiary and secondary amines using cheap AIBN.

The electrochemical α-cyanation of tertiary and secondary amines has been developed by using a cheap cyanide reagent, azobisisobutyronitrile (AIBN). The CN radical, generated through n-Bu4NBr-meidated electrochemical oxidation, participates in a novel α-cyanation reaction under exogenous oxidant-free conditions.

MSA-Regularized Protein Sequence Transformer toward Predicting Genome-Wide Chemical-Protein Interactions: Application to GPCRome Deorphanization.

Small molecules play a critical role in modulating biological systems. Knowledge of chemical-protein interactions helps address fundamental and practical questions in biology and medicine. However, with the rapid emergence of newly sequenced genes, the endogenous or surrogate ligands of a vast number of proteins remain unknown. Homology modeling and machine learning are two major methods for assigning new ligands to a protein but mostly fail when sequence homology between an unannotated protein and those with known functions or structures is low. In this study, we develop a new deep learning framework to predict chemical binding to evolutionary divergent unannotated proteins, whose ligand cannot be reliably predicted by existing methods. By incorporating evolutionary information into self-supervised learning of unlabeled protein sequences, we develop a novel method, distilled sequence alignment embedding (DISAE), for the protein sequence representation. DISAE can utilize all protein sequences and their multiple sequence alignment (MSA) to capture functional relationships between proteins without the knowledge of their structure and function. Followed by the DISAE pretraining, we devise a module-based fine-tuning strategy for the supervised learning of chemical-protein interactions. In the benchmark studies, DISAE significantly improves the generalizability of machine learning models and outperforms the state-of-the-art methods by a large margin. Comprehensive ablation studies suggest that the use of MSA, sequence distillation, and triplet pretraining critically contributes to the success of DISAE. The interpretability analysis of DISAE suggests that it learns biologically meaningful information. We further use DISAE to assign ligands to human orphan G-protein coupled receptors (GPCRs) and to cluster the human GPCRome by integrating their phylogenetic and ligand relationships. The promising results of DISAE open an avenue for exploring the chemical landscape of entire sequenced genomes.

Epidemiological and clinical characteristics of respiratory viruses in 4403 pediatric patients from multiple hospitals in Guangdong, China.

BACKGROUND: Acute respiratory infections (ARI) cause considerable morbidity and mortality worldwide, especially in children. Unfortunately, there are limited multi-center data on common viral respiratory infections in south China. METHODS: A total of 4403 nasal swabs were collected from children in 10 cities in Guangdong, China in 2019. Seven respiratory viruses, influenza A virus (IFA), influenza B virus (IFB), respiratory syncytial virus (RSV), adenoviruses (ADV) and parainfluenza virus types 1-3 (PIV1, PIV2 and PIV3), were detected by direct immunofluorescence antibody assay. The personal information and clinical characteristics were recorded and analyzed. RESULTS: The results showed that at least one virus was detected in 1099 (24.96 %) samples. The detection rates of RSV, IFA, ADV, PIV3, PIV1 and PIV2 were 7.13 % (314/4403), 5.31 % (234/4403), 4.02 % (177/4403), 3.04 % (134/4403), 1.70 % (75/4403) and 1.16 % (51/4403), respectively. The detection rate of RSV was highest in 0-6-month-old children at 18.18 % (106/583), while the detection rate of IFA was highest in 12-18-year-old children at 20.48 % (17/83). The total detection rates in winter and spring were 35.67 % (219/614) and 34.56 % (403/1166), higher than those in summer, 17.41 % (284/1631), and autumn, 19.46 % (193/992). CONCLUSIONS: RSV and IFA were the main respiratory viruses in children. With increasing age the detection rate of RSV decreased in children, but the trends for the detection rates of IFA and IFB were the opposite. This study provided the viral etiology and epidemiology of pediatric patients with ARI in Guangdong, China.

Effects and Safety of Sitagliptin in Non-Alcoholic Fatty Liver Disease: A Systematic Review and Meta-Analysis.

Non-alcoholic fatty liver disease (NAFLD) is currently the most common cause of chronic liver disease. However, the treatment is limited. The aim of this meta-analysis was to evaluate the effects and safety of sitagliptin, a selective inhibitor of dipeptidyl peptidase-4 (DPP-4I), in treating NAFLD. Studies were sourced from electronic databases including PubMed, CENTRAL (Cochrane Controlled Trials Register), Embase, Medline, Web of Science, Clinical Trials, and CNKI to identify all randomized controlled clinical trials (RCTs) and non-RCTs in adult patients with NAFLD. Key outcomes were changes in serum levels of liver enzymes and improvement in hepatic histology and fat content measured by imaging or liver biopsy. Stata14.0 and RevMan5.3 were used for the meta-analysis. Seven studies with 269 NAFLD patients were included. Compared to the control group, sitagliptin treatment improved serum gamma-glutamyl transpeptidase (GGT) levels in the RCT subgroup (SMD = 0.79, 95% CI: 0.01-1.58). However, there was no significant improvement in serum alanine aminotransferase (ALT) or aspartate aminotransferase (AST) levels following sitagliptin treatment. Four of the included studies performed liver imaging, but sitagliptin treatment did not result in a significant reduction in liver fat content. Only five participants developed sitagliptin-related gastrointestinal discomfort. Our study suggests that sitagliptin effects individuals with NAFLD by improving serum GGT. Although sitagliptin is safe and well tolerated in NAFLD patients, it exerts no beneficial effects on liver transaminase and liver fat content in these patients.

The Influence of Different Triazole Antifungal Agents on the Pharmacokinetics of Cyclophosphamide.

Background: Cyclophosphamide is one of the most important chemotherapeutic drugs. Known as a widely accepted treatment strategy, chemotherapy may damage the immune function of cancer patients; as a result, invasive fungal infections (IFIs) occur. Triazole antifungal agents are the most acceptable drugs for IFI treatment, especially those infections caused by chemotherapy. Objective: We aimed to investigate the effects of different triazole antifungal drugs, including fluconazole, itraconazole, and ketoconazole, on the pharmacokinetics (PK) of cyclophosphamide. In addition, we also characterize the potential drug-drug interactions (DDIs) between cyclophosphamide and various triazole antifungal drugs. Methods: The necessary pharmacokinetic parameters and physicochemical data were obtained from published studies. Physiologically based pharmacokinetic (PBPK) models were developed and validated in virtual subjects using Simcyp software. The validated PBPK models were used to evaluate potential DDIs between cyclophosphamide and different triazole antifungal agents in cancer patients. Triazole antifungal agents were simulated by oral administration, whereas cyclophosphamide was simulated by intravenous administration. Results: Simulated plasma concentration-time curves of fluconazole, itraconazole, ketoconazole, and cyclophosphamide were in good consistency with the observed profiles. Our results suggested that the pharmacokinetic parameters of cyclophosphamide were increased by various extents when coadministered with different triazole antifungals. The area under the plasma concentration-time curve of cyclophosphamide was increased when combined with fluconazole, itraconazole, or ketoconazole. Conclusions and Relevance: Ketoconazole had the greatest effect on the PK of cyclophosphamide among the 3 triazole antifungals. Our study provides clues that the toxicity and adverse drug reactions that are associated with cyclophosphamide should be closely monitored when coadministered with ketoconazole.

Fully spin-polarized quadratic non-Dirac bands realized quantum anomalous Hall effect.

The quantum anomalous Hall effect is an intriguing quantum state that exhibits chiral edge states in the absence of a magnetic field. While the search for quantum anomalous Hall insulators is still active, researchers mainly search for the systems containing a magnetic atom. Here, based on first-principles density functional theory, we predict a new family of Chern insulators with fully spin-polarized quadratic px,y non-Dirac bands in the alkaline earth metal BaX (X = Si, Ge, and Sn) system. We show that BaX monolayer has a half-metallic ferromagnetic ground state. The ferromagnetism mainly originates from the p orbitals of Si, Ge and Sn atoms. The 2D BaSn monolayer exhibits a large magnetocrystalline anisotropic energy of 12.20 meV per cell and a nontrivial band gap of 159.10 meV. Interestingly, both the chiral edge current direction and the sign of Chern number can be tuned by doping. Furthermore, the 4% compressive strain in the 2D BaX systems can drive a structural phase transition but the nontrivial topological properties remain reserved. Our findings not only extend the novel topological physics but also provide fascinating opportunities for the realization of the quantum anomalous Hall effect experimentally.