Trastuzumab Deruxtecan after Endocrine Therapy in Metastatic Breast Cancer.

BACKGROUND: Outcomes in patients with hormone receptor-positive metastatic breast cancer worsen after one or more lines of endocrine-based therapy. Trastuzumab deruxtecan has shown efficacy in patients with metastatic breast cancer with low expression of human epidermal growth factor receptor 2 (HER2) after previous chemotherapy. METHODS: We conducted a phase 3, multicenter, open-label trial involving patients with hormone receptor-positive metastatic breast cancer with low HER2 expression (a score of 1+ or 2+ on immunohistochemical [IHC] analysis and negative results on in situ hybridization) or ultralow HER2 expression (IHC 0 with membrane staining) who had received one or more lines of endocrine-based therapy and no previous chemotherapy for metastatic breast cancer. Patients were randomly assigned in a 1:1 ratio to receive trastuzumab deruxtecan or the physician's choice of chemotherapy. The primary end point was progression-free survival (according to blinded independent central review) among the patients with HER2-low disease. Secondary end points included progression-free survival among all the patients who had undergone randomization, overall survival, and safety. RESULTS: Of the 866 patients who underwent randomization, 713 had HER2-low disease, and 153 had HER2-ultralow disease. Among the patients with HER2-low disease, the median progression-free survival was 13.2 months (95% confidence interval [CI], 11.4 to 15.2) in the trastuzumab deruxtecan group and 8.1 months (95% CI, 7.0 to 9.0) in the chemotherapy group (hazard ratio for disease progression or death, 0.62; 95% CI, 0.51 to 0.74; P<0.001); the results were consistent in the exploratory HER2-ultralow population. Data for overall survival were immature. Adverse events of grade 3 or higher occurred in 52.8% of the patients in the trastuzumab deruxtecan group and in 44.4% of those in the chemotherapy group. Adjudicated interstitial lung disease or pneumonitis occurred in 49 patients (11.3%; three events were grade 5 in severity) and in 1 patient (0.2%; grade 2), respectively. CONCLUSIONS: Among patients with hormone receptor-positive, HER2-low or HER2-ultralow metastatic breast cancer who had received one or more lines of endocrine-based therapy, treatment with trastuzumab deruxtecan resulted in longer progression-free survival than chemotherapy. No new safety signals were identified. (Funded by AstraZeneca and Daiichi Sankyo; DESTINY-Breast06 ClinicalTrials.gov number, NCT04494425.).

Differentiated roles of Lifshitz transition on thermodynamics and superconductivity in La2-xSrxCuO4.

The effect of Lifshitz transition on thermodynamics and superconductivity in hole-doped cuprates has been heavily debated but remains an open question. In particular, an observed peak of electronic specific heat is proposed to originate from fluctuations of a putative quantum critical point p* (e.g., the termination of pseudogap at zero temperature), which is close to but distinguishable from the Lifshitz transition in overdoped La-based cuprates where the Fermi surface transforms from hole-like to electron-like. Here we report an in situ angle-resolved photoemission spectroscopy study of three-dimensional Fermi surfaces in La2-xSrxCuO4 thin films (x = 0.06 to 0.35). With accurate kz dispersion quantification, the said Lifshitz transition is determined to happen within a finite range around x = 0.21. Normal state electronic specific heat, calculated from spectroscopy-derived band parameters, reveals a doping-dependent profile with a maximum at x = 0.21 that agrees with previous thermodynamic microcalorimetry measurements. The account of the specific heat maximum by underlying band structures excludes the need for additionally dominant contribution from the quantum fluctuations at p*. A d-wave superconducting gap smoothly across the Lifshitz transition demonstrates the insensitivity of superconductivity to the dramatic density of states enhancement.

Prediction of blood-brain barrier penetrating peptides based on data augmentation with Augur.

BACKGROUND: The blood-brain barrier serves as a critical interface between the bloodstream and brain tissue, mainly composed of pericytes, neurons, endothelial cells, and tightly connected basal membranes. It plays a pivotal role in safeguarding brain from harmful substances, thus protecting the integrity of the nervous system and preserving overall brain homeostasis. However, this remarkable selective transmission also poses a formidable challenge in the realm of central nervous system diseases treatment, hindering the delivery of large-molecule drugs into the brain. In response to this challenge, many researchers have devoted themselves to developing drug delivery systems capable of breaching the blood-brain barrier. Among these, blood-brain barrier penetrating peptides have emerged as promising candidates. These peptides had the advantages of high biosafety, ease of synthesis, and exceptional penetration efficiency, making them an effective drug delivery solution. While previous studies have developed a few prediction models for blood-brain barrier penetrating peptides, their performance has often been hampered by issue of limited positive data. RESULTS: In this study, we present Augur, a novel prediction model using borderline-SMOTE-based data augmentation and machine learning. we extract highly interpretable physicochemical properties of blood-brain barrier penetrating peptides while solving the issues of small sample size and imbalance of positive and negative samples. Experimental results demonstrate the superior prediction performance of Augur with an AUC value of 0.932 on the training set and 0.931 on the independent test set. CONCLUSIONS: This newly developed Augur model demonstrates superior performance in predicting blood-brain barrier penetrating peptides, offering valuable insights for drug development targeting neurological disorders. This breakthrough may enhance the efficiency of peptide-based drug discovery and pave the way for innovative treatment strategies for central nervous system diseases.

Advanced glycation end products promote the progression of endometrial cancer via activating the RAGE/CHKA/PI3K/AKT signaling pathway.

Endometrial cancer (EC) is a common malignant tumor that is closely associated with metabolic disorders such as diabetes and obesity. Advanced glycation end products (AGEs) are complex polymers formed by the reaction of reducing sugars with the amino groups of biomacromolecules, mediating the occurrence and development of many chronic metabolic diseases. Recent research has demonstrated that the accumulation of AGEs can affect the tumor microenvironment, metabolism, and signaling pathways, thereby affecting the malignant progression of tumors. However, the mechanism by which AGEs affect EC is unclear. Our research aimed to investigate how AGEs promote the development of EC through metabolic pathways and to explore their potential underlying mechanisms. Our experimental results demonstrated that AGEs upregulated the choline metabolism mediated by choline kinase alpha (CHKA) through the receptor for advanced glycation end products (RAGE), activating the PI3K/AKT pathway and enhancing the malignant biological behavior of EC cells. Virtual screening and molecular dynamics simulation revealed that timosaponin A3 (timo A3) could target CHKA to inhibit AGE-induced progression of EC and that a newly discovered CHKA inhibitor could be a novel targeted inhibitor for the treatment of EC. This study provides new therapeutic strategies and contributes to the treatment of EC.

Ammonia-Induced FCC Ru Nanocrystals for Efficient Alkaline Hydrogen Electrocatalysis.

The urgent development of effective electrocatalysts for hydrogen evolution and hydrogen oxidation reaction (HER/HOR) is needed due to the sluggish alkaline hydrogen electrocatalysis. Here, an unusual face-centered cubic (fcc) Ru nanocrystal with favorable HER/HOR performance is offered. Guided by the lower calculated surface energy of fcc Ru than that of hcp Ru in NH3, the carbon-supported fcc Ru electrocatalyst is facilely synthesized in the NH3 reducing atmosphere. The specific HOR kinetic current density of fcc Ru can reach 23.4 mA cmPGM -2, which is around 20 and 21 times greater than that of hexagonal close-packed (hcp) Ru and Pt/C, respectively. Additionally, the HER specific activity is enhanced more than six times in fcc Ru electrocatalyst when compared to Pt/C. Experimental and theoretical analysis indicate that the phase transition from hcp Ru to fcc Ru can negatively shift the d band center, weaken the interaction between catalysts and key intermediates and therefore enhances the HER/HOR kinetics.

iRice-MS: An integrated XGBoost model for detecting multitype post-translational modification sites in rice.

Post-translational modification (PTM) refers to the covalent and enzymatic modification of proteins after protein biosynthesis, which orchestrates a variety of biological processes. Detecting PTM sites in proteome scale is one of the key steps to in-depth understanding their regulation mechanisms. In this study, we presented an integrated method based on eXtreme Gradient Boosting (XGBoost), called iRice-MS, to identify 2-hydroxyisobutyrylation, crotonylation, malonylation, ubiquitination, succinylation and acetylation in rice. For each PTM-specific model, we adopted eight feature encoding schemes, including sequence-based features, physicochemical property-based features and spatial mapping information-based features. The optimal feature set was identified from each encoding, and their respective models were established. Extensive experimental results show that iRice-MS always display excellent performance on 5-fold cross-validation and independent dataset test. In addition, our novel approach provides the superiority to other existing tools in terms of AUC value. Based on the proposed model, a web server named iRice-MS was established and is freely accessible at http://lin-group.cn/server/iRice-MS.

Anemoside B4, a new pyruvate carboxylase inhibitor, alleviates colitis by reprogramming macrophage function.

OBJECTIVES: Colitis is a global disease usually accompanied by intestinal epithelial damage and intestinal inflammation, and an increasing number of studies have found natural products to be highly effective in treating colitis. Anemoside B4 (AB4), an abundant saponin isolated from Pulsatilla chinensis (Bunge), which was found to have strong anti-inflammatory activity. However, the exact molecular mechanisms and direct targets of AB4 in the treatment of colitis remain to be discovered. METHODS: The anti-inflammatory activities of AB4 were verified in LPS-induced cell models and 2, 4, 6-trinitrobenzene sulfonic (TNBS) or dextran sulfate sodium (DSS)-induced colitis mice and rat models. The molecular target of AB4 was identified by affinity chromatography analysis using chemical probes derived from AB4. Experiments including proteomics, molecular docking, biotin pull-down, surface plasmon resonance (SPR), and cellular thermal shift assay (CETSA) were used to confirm the binding of AB4 to its molecular target. Overexpression of pyruvate carboxylase (PC) and PC agonist were used to study the effects of PC on the anti-inflammatory and metabolic regulation of AB4 in vitro and in vivo. RESULTS: AB4 not only significantly inhibited LPS-induced NF-κB activation and increased ROS levels in THP-1 cells, but also suppressed TNBS/DSS-induced colonic inflammation in mice and rats. The molecular target of AB4 was identified as PC, a key enzyme related to fatty acid, amino acid and tricarboxylic acid (TCA) cycle. We next demonstrated that AB4 specifically bound to the His879 site of PC and altered the protein's spatial conformation, thereby affecting the enzymatic activity of PC. LPS activated NF-κB pathway and increased PC activity, which caused metabolic reprogramming, while AB4 reversed this phenomenon by inhibiting the PC activity. In vivo studies showed that diisopropylamine dichloroacetate (DADA), a PC agonist, eliminated the therapeutic effects of AB4 by changing the metabolic rearrangement of intestinal tissues in colitis mice. CONCLUSION: We identified PC as a direct cellular target of AB4 in the modulation of inflammation, especially colitis. Moreover, PC/pyruvate metabolism/NF-κB is crucial for LPS-driven inflammation and oxidative stress. These findings shed more light on the possibilities of PC as a potential new target for treating colitis.

Association between triglyceride glucose-waist to height ratio and coronary heart disease: a population-based study.

BACKGROUND: The Triglyceride glucose (TyG) index-related indicators improve risk stratification by identifying individuals prone to atherosclerosis early in life. This study aimed to examine the relation between TyG-waist circumference-to-height ratio (TyG-WHtR) and coronary heart disease. METHODS: Data from four National Health and Nutrition Examination Surveys (NHANES) cycles between 2011 and 2018 were used for a cross-sectional study. The association between TyG-WHtR and coronary heart disease risk was examined using a multifactorial logistic regression model, and corresponding subgroup analyses were performed. Nonlinear correlations were analyzed using smooth curve fitting and threshold effects analysis. When nonlinear connections were discovered, appropriate inflection points were investigated using recursive methods. RESULTS: TyG-WHtR and coronary heart disease were significantly positively correlated in the multifactorial logistic regression analysis. Subgroup analyses and interaction tests revealed that gender, age, smoking status, and cancer were not significantly associated with this correlation (P for interaction > 0.05). Furthermore, utilizing threshold effect analysis and smooth curve fitting, a nonlinear connection with an inflection point of 0.36 was observed between TyG-WHtR and coronary heart disease. CONCLUSIONS: According to this study, the American population is far more likely to have coronary heart disease if they have higher TyG-WHtR levels.

Association between cooking patterns and the prevalence of hyperlipidemia in Eastern China.

BACKGROUND: Hyperlipidemia is a major risk factor for many diseases. Previous studies have shown that diet is closely associated with hyperlipidemia. However, the relationship between cooking methods and hyperlipidemia remains unclear. The objective of this study was to identify the major cooking patterns existing in the Eastern Chinese population and evaluate their association with the prevalence of hyperlipidemia. METHODS: We interviewed 4,710 residents in Eastern China regarding the consumption frequency of each cooking method when they prepare food at home or when eating out and regarding the prevalence of hyperlipidemia. Factor analysis, Chi-square tests, analysis of variance, and binary logistic regression analysis were used to identify the cooking patterns and analyze the characteristics of participants' categories of cooking patterns and the relationship between different cooking patterns and prevalence of hyperlipidemia. RESULTS: Three major cooking patterns were identified: Traditional Chinese, Bland (little or no oil is used to process the food), and High-temperature cooking patterns. After controlling for potential confounders, participants in the highest quartile of the Bland cooking pattern had lower odds of hyperlipidemia than those in the lowest quartile. Nevertheless, no significant associations were observed between the Traditional Chinese and High-temperature cooking patterns and the prevalence of hyperlipidemia. CONCLUSIONS: This study confirms the association between cooking patterns and the prevalence of hyperlipidemia and indicates that the Bland cooking pattern is associated with a reduced prevalence of hyperlipidemia.

Curcumin attenuated neuroinflammation via the TLR4/MyD88/NF-κB signaling way in the juvenile rat hippocampus following kainic acid-induced epileptic seizures.

The study examined curcumin's impart on relieving neuroinflammation of juvenile rats in kainic acid (KA) induced epileptic seizures by inhibiting the TLR4/MyD88/NF-κB pathway. There were five groups: control, KA, KA + curcumin (KC), KA + oxcarbazepine (OXC) (KO), KA + curcumin + OXC (KCO) groups. KA was stereotactically injected into right hippocampus following intraperitoneal injection of curcumin or (and) OXC for seven days. The rats in the above groups were randomly divided into three subgroups (at 6 h, 24 h, and 72 h of KA administration) following the seizure degree assessed. The number of NeuN (+) neurons and GFAP (+) astrocytes was counted. The gene and protein levels of TLR4, MyD88, and NF-κB were detected. Compared with the KA group, the seizure latency was longer, and the incidence of status epilepticus (SE) was lower in the KC, KO, and KCO groups. The most significant changes were in the KCO group. At 72 h following KA injected, the number of neurons was the least, and the number of astrocytes was the most in the KA group. The number of neurons was the most and the number of astrocytes was the least in the KCO group. At 24 h, the mRNA and protein levels of TLR4, MyD88, and NF-κB in the KA group were the most. The above valves were the least in the KCO group. Therefore, curcumin could enhance anti-epileptic effect of OXC, protect injured neurons and reduce proliferated glial cells of the hippocampus of epileptic rats by inhibiting inflammation via the TLR4/MyD88/NF-κB pathway.

Dynamics and concordance alterations of regional brain function indices in vestibular migraine: a resting-state fMRI study.

BACKGROUND: Prior MRI studies on vestibular migraine (VM) have revealed abnormalities in static regional intrinsic brain activity (iBA) and dynamic functional connectivity between brain regions or networks. However, the temporal variation and concordance of regional iBA measures remain to be explored. METHODS: 57 VM patients during the interictal period were compared to 88 healthy controls (HC) in this resting-state functional magnetic resonance imaging (fMRI) study. The dynamics and concordance of regional iBA indices, including amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo), were examined by utilizing sliding time-window analysis. Partial correlation analyses were performed between clinical parameters and resting-state fMRI indices in brain areas showing significant group differences. RESULTS: The VM group showed increased ALFF and ReHo dynamics, as well as increased temporal concordance between ALFF and ReHo in the bilateral paracentral lobule and supplementary motor area relative to the HC group. We also found decreased ReHo dynamics in the right temporal pole, and decreased ALFF dynamics in the right cerebellum posterior lobe, bilateral angular gyrus and middle occipital gyrus (MOG) in the VM group compared with the HC group. Moreover, a positive correlation was observed between ALFF dynamics in the left MOG and vertigo disease duration across all VM patients. CONCLUSION: Temporal dynamics and concordance of regional iBA indices were altered in the motor cortex, cerebellum, occipital and temporoparietal cortex, which may contribute to disrupted multisensory processing and vestibular control in patients with VM. ALFF dynamics in the left MOG may be useful biomarker for evaluating vertigo burden in this disorder.

Disrupted gray matter connectome in vestibular migraine: a combined machine learning and individual-level morphological brain network analysis.

BACKGROUND: Although gray matter (GM) volume alterations have been extensively documented in previous voxel-based morphometry studies on vestibular migraine (VM), little is known about the impact of this disease on the topological organization of GM morphological networks. This study investigated the altered network patterns of the GM connectome in patients with VM. METHODS: In this study, 55 patients with VM and 57 healthy controls (HCs) underwent structural T1-weighted MRI. GM morphological networks were constructed by estimating interregional similarity in the distributions of regional GM volume based on the Kullback-Leibler divergence measure. Graph-theoretical metrics and interregional morphological connectivity were computed and compared between the two groups. Partial correlation analyses were performed between significant GM connectome features and clinical parameters. Logistic regression (LR), support vector machine (SVM), and random forest (RF) classifiers were used to examine the performance of significant GM connectome features in distinguishing patients with VM from HCs. RESULTS: Compared with HCs, patients with VM exhibited increased clustering coefficient and local efficiency, as well as reduced nodal degree and nodal efficiency in the left superior temporal gyrus (STG). Furthermore, we identified one connected component with decreased morphological connectivity strength, and the involved regions were mainly located in the STG, temporal pole, prefrontal cortex, supplementary motor area, cingulum, fusiform gyrus, and cerebellum. In the VM group, several connections in the identified connected component were correlated with clinical measures (i.e., symptoms and emotional scales); however, these correlations did not survive multiple comparison corrections. A combination of significant graph- and connectivity-based features allowed single-subject classification of VM versus HC with significant accuracy of 77.68%, 77.68%, and 72.32% for the LR, SVM, and RF models, respectively. CONCLUSION: Patients with VM had aberrant GM connectomes in terms of topological properties and network connections, reflecting potential dizziness, pain, and emotional dysfunctions. The identified features could serve as individualized neuroimaging markers of VM.

Hypoxic preconditioned mesenchymal stem cells ameliorate rat brain injury after cardiopulmonary resuscitation by suppressing neuronal pyroptosis.

Cardiac arrest (CA) can result in cerebral ischaemia-reperfusion injury and poor neurological outcomes. While bone marrow-derived mesenchymal stem cells (BMSCs) have been shown to have protective effects in brain ischaemic disease, their efficacy can be reduced by the poor oxygen environment. In this study, we investigated the neuroprotective effects of hypoxic preconditioned BMSCs (HP-BMSCs) and normoxic BMSCs (N-BMSCs) in a cardiac arrest rat model by examining their ability to ameliorate cell pyroptosis. The mechanism underlying the process was also explored. Cardiac arrest was induced in rats for 8 min and surviving rats received 1 × 106 normoxic/hypoxic BMSCs or PBS via intracerebroventricular (ICV) transplantation. Neurological function of rats was evaluated using neurological deficit scores (NDSs) and examined for brain pathology. Serum S100B and neuron-specific enolase (NSE) levels and cortical proinflammatory cytokines were measured to evaluate brain injury. Pyroptosis-related proteins in the cortex after cardiopulmonary resuscitation (CPR) were measured using western blotting and immunofluorescent staining. Transplanted BMSCs were tracked using bioluminescence imaging. Results showed significantly better neurological function and neuropathological damage after transplantation with HP-BMSCs. In addition, HP-BMSCs reduced levels of pyroptosis-related proteins in the rat cortex after CPR and significantly reduced levels of biomarkers for brain injury. Mechanistically, HP-BMSCs alleviated brain injury by reducing the expressions of HMGB1, TLR4, NF-κB p65, p38 MAPK and JNK in the cortex. Our study demonstrated that hypoxic preconditioning could enhance the efficacy of BMSCs in alleviating post-resuscitation cortical pyroptosis. This effect may be related to the regulation of the HMGB1/TLR4/NF-κB, MAPK signalling pathways.

Analysis of long noncoding RNAs and messenger RNAs expression profiles in the hearts of mice with acute viral myocarditis.

Acute viral myocarditis (AVMC) is a common acute myocardial inflammation caused by viral infections, which can lead to severe cardiac dysfunction. Several long noncoding RNAs (lncRNAs) with aberrant expression have been identified in the pathogenesis of AVMC. However, the expression profiles and functions of lncRNAs in AVMC have not been fully elucidated. In the present study, we constructed AVMC mouse models by intraperitoneal injection of coxsackievirus B3 (CVB3) and performed RNA sequencing (RNA-seq) on heart tissues to investigate the differences in lncRNAs and messenger RNAs (mRNAs) expression profiles. Based on the cutoff criteria of adjusted p-values (padj) <0.05 and |log2FoldChange| >1, a total of 1122 differentially expressed lncRNAs (DElncRNAs) and 3186 differentially expressed mRNAs (DEmRNAs) were screened, including 734 upregulated and 388 downregulated lncRNAs, 1821 upregulated and 1365 downregulated mRNAs. RT-qPCR analysis validated that the expression patterns of 12 randomly selected genes (6 DElncRNAs and 6 DEmRNAs) were highly consistent with those in RNA-seq, proving the reliability of the RNA-seq data. Then, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses revealed that differentially expressed genes were mainly involved in metabolic and immune-related processes. Furthermore, co-expression networks between DElncRNAs and DEmRNAs in cytokine-cytokine receptor interaction, MAPK signaling pathway, and PI3K-Akt signaling pathway were constructed to study the molecular interactions of these molecules. Our study, for the first time, reveals the expression profiles of lncRNAs and mRNAs associated with AVMC, which may shed light on the roles of lncRNAs in disease pathogenesis and aid in discovering new therapeutic targets.

The efficacy and safety of selective RET inhibitors in RET fusion-positive non-small cell lung cancer: a meta-analysis.

BACKGROUND: Rearranged during transfection (RET) fusion-positive occurs in approximately 2% of non-small cell lung cancer (NSCLC). This mutation often predicts metastasis risk and poor prognosis, and current mainstream therapies provide limited patient benefit. Selective RET inhibitors Pralsetinib and Selpercatinib are targeted drugs approved by the US Food and Drug Administration for treating RET-mutated tumors. The phase I/II clinical trial results of their treatment of NSCLC have been published. However, the clinical effect of selective RET inhibitors on RET fusion-positive NSCLC remains controversial. Purpose Meta-analysis was performed to investigate the efficacy and safety of selective RET inhibitors in treating RET fusion-positive NSCLC. Methods Qualified literature was searched in Pubmed, Cochrane Library, Embase, and Web of Science. Outcomes included objective response rate (ORR), median progression-free survival (mPFS), disease control rate (DCR), intracranial ORR, and adverse events. Stata 15.1 software was used to analyze the data. Results A total of 8 studies were included in this meta-analysis. The combined results showed that the ORR of patients treated with selective RET inhibitors was 67% (95% confidence interval:0.64 to 0.70, P < 0.01), DCR was 92% (95%CI: 0.91-0.94, P < 0.01), the mPFS was 16.09 months (95%CI: 11.66-20.52, P < 0.01). In treated patients with RET mutation, the intracranial ORR was 86% (95%CI:0.74 ~ 0.96, P < 0.01). ORR in untreated patients was more effective than untreated patients [HR = 0.44 (95%CI: 0.35-0.56, P < 0.01)]. The major adverse events (grade 3-4) are neutropenia (13%) and anaemia (13%). Conclusions Selective RET inhibitors Pralsetinib and Selpercatinib have shown a good effect on RET fusion-positive NSCLC, with a low incidence of adverse events.

Submicron resolution techniques: Multiphoton microscopy in skin disease.

Non-invasive optical examination plays a crucial role in various aspects of dermatology, such as diagnosis, management and research. Multiphoton microscopy uses a unique submicron technology to stimulate autofluorescence (AF), allowing for the observation of cellular structure, assessment of redox status and quantification of collagen fibres. This advanced imaging technique offers dermatologists novel insights into the skin's structure, positioning it as a promising 'stethoscope' for future development in the field. This review provides an overview of multiphoton microscopy's principles, technology and application in studying normal skin, tumour and inflammatory diseases, as well as collagen-related and pigmentary diseases.

Enhancing Molecular Representations Via Graph Transformation Layers.

Molecular representation learning is an essential component of many molecule-oriented tasks, such as molecular property prediction and molecule generation. In recent years, graph neural networks (GNNs) have shown great promise in this area, representing a molecule as a graph composed of nodes and edges. There are increasing studies showing that coarse-grained or multiview molecular graphs are important for molecular representation learning. Most of their models, however, are too complex and lack flexibility in learning different granular information for different tasks. Here, we proposed a flexible and simple graph transformation layer (i.e., LineEvo), a plug-and-use module for GNNs, which enables molecular representation learning from multiple perspectives. The LineEvo layer transforms fine-grained molecular graphs into coarse-grained ones based on the line graph transformation strategy. Especially, it treats the edges as nodes and generates the new connected edges, atom features, and atom positions. By stacking LineEvo layers, GNNs can learn multilevel information, from atom-level to triple-atoms level and coarser level. Experimental results show that the LineEvo layers can improve the performance of traditional GNNs on molecular property prediction benchmarks on average by 7%. Additionally, we show that the LineEvo layers can help GNNs have more expressive power than the Weisfeiler-Lehman graph isomorphism test.

iPADD: A Computational Tool for Predicting Potential Antidiabetic Drugs Using Machine Learning Algorithms.

Diabetes mellitus is a chronic metabolic disease, which causes an imbalance in blood glucose homeostasis and further leads to severe complications. With the increasing population of diabetes, there is an urgent need to develop drugs to treat diabetes. The development of artificial intelligence provides a powerful tool for accelerating the discovery of antidiabetic drugs. This work aims to establish a predictor called iPADD for discovering potential antidiabetic drugs. In the predictor, we used four kinds of molecular fingerprints and their combinations to encode the drugs and then adopted minimum-redundancy-maximum-relevance (mRMR) combined with an incremental feature selection strategy to screen optimal features. Based on the optimal feature subset, eight machine learning algorithms were applied to train models by using 5-fold cross-validation. The best model could produce an accuracy (Acc) of 0.983 with the area under the receiver operating characteristic curve (auROC) value of 0.989 on an independent test set. To further validate the performance of iPADD, we selected 65 natural products for case analysis, including 13 natural products in clinical trials as positive samples and 52 natural products as negative samples. Except for abscisic acid, our model can give correct prediction results. Molecular docking illustrated that quercetin and resveratrol stably bound with the diabetes target NR1I2. These results are consistent with the model prediction results of iPADD, indicating that the machine learning model has a strong generalization ability. The source code of iPADD is available at https://github.com/llllxw/iPADD.

Identification of adaptor proteins using the ANOVA feature selection technique.

The adaptor proteins play a crucially important role in regulating lymphocyte activation. Rapid and efficient identification of adaptor proteins is essential for understanding their functions. However, biochemical methods require not only expensive experimental costs, but also long experiment cycles and more personnel. Therefore, a computational method that could accurately identify adaptor proteins is urgently needed. To solve this issue, we developed a classifier that combined the support vector machine (SVM) with the composition of k-Spaced Amino Acid Pairs (CKSAAP) and the amino acid composition (AAC) to identify adaptor proteins. Analysis of variance (ANOVA) was used to select the optimized features which could generate the maximum prediction performance. By examining the proposed model on independent data, we found that the 447 optimized features could achieve an accuracy of 92.39% with an AUC of 0.9766, demonstrating the powerful capabilities of our model. We hope that the proposed model could provide more clues for studying adaptor proteins.

Altered functional connectivity within and between resting-state networks in patients with vestibular migraine.

PURPOSE: Previous functional magnetic resonance imaging studies have substantiated changes in multiple brain regions of functional activity in patients with vestibular migraine. However, few studies have assessed functional connectivity within and between specific brain networks in vestibular migraine. METHODS: Our study subjects included 37 patients with vestibular migraine and 35 healthy controls, and the quality of magnetic resonance images of all subjects met the requirements. Independent component analysis was performed to identify resting-state networks, and we investigated changes in functional connectivity patterns within and between brain networks. We also used Pearson correlation analysis to assess the relationship between changes in functional connectivity and the clinical features of patients with vestibular migraine. RESULTS: A total of 14 independent components were identified. Compared to healthy controls, patients with vestibular migraine exhibited decreased intra-network functional connectivity in the executive control network and weakened functional connectivity between the anterior default mode network and the ventral attention network, between the anterior default mode network and the salience network, and between the right frontoparietal network and the auditory network. Moreover, the functional connectivity between the salience network and the dorsal attention network was increased. However, the functional connectivity of networks and clinical characteristics of vestibular migraine patients did not demonstrate any significant correlation. CONCLUSION: In conclusion, our study suggested that patients with vestibular migraine also have abnormal multisensory integration during the interictal period and that the attention network is involved. Changing within- and between-network functional connectivity may indicate that vestibular cortex areas are in a sensitive state.