Reducing acetylated tau is neuroprotective in brain injury.

Traumatic brain injury (TBI) is the largest non-genetic, non-aging related risk factor for Alzheimer's disease (AD). We report here that TBI induces tau acetylation (ac-tau) at sites acetylated also in human AD brain. This is mediated by S-nitrosylated-GAPDH, which simultaneously inactivates Sirtuin1 deacetylase and activates p300/CBP acetyltransferase, increasing neuronal ac-tau. Subsequent tau mislocalization causes neurodegeneration and neurobehavioral impairment, and ac-tau accumulates in the blood. Blocking GAPDH S-nitrosylation, inhibiting p300/CBP, or stimulating Sirtuin1 all protect mice from neurodegeneration, neurobehavioral impairment, and blood and brain accumulation of ac-tau after TBI. Ac-tau is thus a therapeutic target and potential blood biomarker of TBI that may represent pathologic convergence between TBI and AD. Increased ac-tau in human AD brain is further augmented in AD patients with history of TBI, and patients receiving the p300/CBP inhibitors salsalate or diflunisal exhibit decreased incidence of AD and clinically diagnosed TBI.

In Situ TEM Characterization and Modulation for Phase Engineering of Nanomaterials.

Solid-state phase transformation is an intriguing phenomenon in crystalline or noncrystalline solids due to the distinct physical and chemical properties that can be obtained and modified by phase engineering. Compared to bulk solids, nanomaterials exhibit enhanced capability for phase engineering due to their small sizes and high surface-to-volume ratios, facilitating various emerging applications. To establish a comprehensive atomistic understanding of phase engineering, in situ transmission electron microscopy (TEM) techniques have emerged as powerful tools, providing unprecedented atomic-resolution imaging, multiple characterization and stimulation mechanisms, and real-time integrations with various external fields. In this Review, we present a comprehensive overview of recent advances in in situ TEM studies to characterize and modulate nanomaterials for phase transformations under different stimuli, including mechanical, thermal, electrical, environmental, optical, and magnetic factors. We briefly introduce crystalline structures and polymorphism and then summarize phase stability and phase transformation models. The advanced experimental setups of in situ techniques are outlined and the advantages of in situ TEM phase engineering are highlighted, as demonstrated via several representative examples. Besides, the distinctive properties that can be obtained from in situ phase engineering are presented. Finally, current challenges and future research opportunities, along with their potential applications, are suggested.

Manipulation of Moiré Superlattice in Twisted Monolayer-multilayer Graphene by Moving Nanobubbles.

In the heterostructure of two-dimensional (2D) materials, many novel physics phenomena are strongly dependent on the Moiré superlattice. How to achieve the continuous manipulation of the Moiré superlattice in the same sample is very important to study the evolution of various physical properties. Here, in minimally twisted monolayer-multilayer graphene, we found that bubble-induced strain has a huge impact on the Moiré superlattice. By employing the AFM tip to dynamically and continuously move the nanobubble, we realized the modulation of the Moiré superlattice, like the evolution of regular triangular domains into long strip domain structures with single or double domain walls. We also achieved controllable modulation of the Moiré superlattice by moving multiple nanobubbles and establishing the coupling of nanobubbles. Our work presents a flexible method for continuous and controllable manipulation of Moiré superlattices, which will be widely used to study novel physical properties in 2D heterostructures.

Quantitative trait locus (xQTL) approaches identify risk genes and drug targets from human non-coding genomes.

Advances and reduction of costs in various sequencing technologies allow for a closer look at variations present in the non-coding regions of the human genome. Correlating non-coding variants with large-scale multi-omic data holds the promise not only of a better understanding of likely causal connections between non-coding DNA and expression of traits but also identifying potential disease-modifying medicines. Genome-phenome association studies have created large datasets of DNA variants that are associated with multiple traits or diseases, such as Alzheimer's disease; yet, the functional consequences of variants, in particular of non-coding variants, remain largely unknown. Recent advances in functional genomics and computational approaches have led to the identification of potential roles of DNA variants, such as various quantitative trait locus (xQTL) techniques. Multi-omic assays and analytic approaches toward xQTL have identified links between genetic loci and human transcriptomic, epigenomic, proteomic and metabolomic data. In this review, we first discuss the recent development of xQTL from multi-omic findings. We then highlight multimodal analysis of xQTL and genetic data for identification of risk genes and drug targets using Alzheimer's disease as an example. We finally discuss challenges and future research directions (e.g. artificial intelligence) for annotation of non-coding variants in complex diseases.

Multimodal single-cell/nucleus RNA sequencing data analysis uncovers molecular networks between disease-associated microglia and astrocytes with implications for drug repurposing in Alzheimer's disease.

Because disease-associated microglia (DAM) and disease-associated astrocytes (DAA) are involved in the pathophysiology of Alzheimer's disease (AD), we systematically identified molecular networks between DAM and DAA to uncover novel therapeutic targets for AD. Specifically, we develop a network-based methodology that leverages single-cell/nucleus RNA sequencing data from both transgenic mouse models and AD patient brains, as well as drug-target network, metabolite-enzyme associations, the human protein-protein interactome, and large-scale longitudinal patient data. Through this approach, we find both common and unique gene network regulators between DAM (i.e., PAK1, MAPK14, and CSF1R) and DAA (i.e., NFKB1, FOS, and JUN) that are significantly enriched by neuro-inflammatory pathways and well-known genetic variants (i.e., BIN1). We identify shared immune pathways between DAM and DAA, including Th17 cell differentiation and chemokine signaling. Last, integrative metabolite-enzyme network analyses suggest that fatty acids and amino acids may trigger molecular alterations in DAM and DAA. Combining network-based prediction and retrospective case-control observations with 7.2 million individuals, we identify that usage of fluticasone (an approved glucocorticoid receptor agonist) is significantly associated with a reduced incidence of AD (hazard ratio [HR] = 0.86, 95% confidence interval [CI] 0.83-0.89, P < 1.0 × 10-8). Propensity score-stratified cohort studies reveal that usage of mometasone (a stronger glucocorticoid receptor agonist) is significantly associated with a decreased risk of AD (HR = 0.74, 95% CI 0.68-0.81, P < 1.0 × 10-8) compared to fluticasone after adjusting age, gender, and disease comorbidities. In summary, we present a network-based, multimodal methodology for single-cell/nucleus genomics-informed drug discovery and have identified fluticasone and mometasone as potential treatments in AD.

Strain Engineering of Twisted Bilayer Graphene: The Rise of Strain-Twistronics.

The layer-by-layer stacked van der Waals structures (termed vdW hetero/homostructures) offer a new paradigm for materials design-their physical properties can be tuned by the vertical stacking sequence as well as by adding a mechanical twist, stretch, and hydrostatic pressure to the atomic structure. In particular, simple twisting and stacking of two layers of graphene can form a uniform and ordered Moiré superlattice, which can effectively modulate the electrons of graphene layers and lead to the discovery of unconventional superconductivity and strong correlations. However, the twist angle of twisted bilayer graphene (tBLG) is almost unchangeable once the interlayer stacking is determined, while applying mechanical elastic strain provides an alternative way to deeply regulate the electronic structure by controlling the lattice spacing and symmetry. In this review, diverse experimental advances are introduced in straining tBLG by in-plane and out-of-plane modes, followed by the characterizations and calculations toward quantitatively tuning the strain-engineered electronic structures. It is further discussed that the structural relaxation in strained Moiré superlattice and its influence on electronic structures. Finally, the conclusion entails prospects for opportunities of strained twisted 2D materials, discussions on existing challenges, and an outlook on the intriguing emerging field, namely "strain-twistronics".

CCR5 regulates Aß1-42-induced learning and memory deficits in mice.

C-C chemokine receptor 5 (CCR5) is a chemokine receptor involved in immune responses and a co-receptor for HIV infection. Recently, CCR5 has also been reported to play a role in synaptic plasticity, learning and memory, and cognitive deficits associated with normal aging, traumatic brain injury (TBI), and HIV-associated neurocognitive disorder (HAND). In contrast, the role of CCR5 in cognitive deficits associated with other disorders, including Alzheimer's disease (AD), is much less understood. Studies have reported an increase in expression of CCR5 or its ligands in both AD patients and AD rodent models, suggesting a correlation between AD and CCR5 expression. However, whether blocking CCR5 in specific brain regions, such as the hippocampus, could improve memory deficits in AD mouse models is unknown. To study the potential causal role of CCR5 in cognitive deficits in AD, we injected soluble Aß1-42 or a control (Aß42-1) oligomers in the dorsal CA1 region of the hippocampus and found that Aß1-42 injection resulted in severe memory impairment in the object place recognition (OPR) and novel object recognition (NOR) tests. Aß1-42 injection caused an increase in Ccr5, Ccl3, and Ccl4 in the dorsal hippocampus, and the expression levels of CCR5 and its ligands remained elevated at 2 weeks after Aß1-42 injection. Knocking down Ccr5 in the CA1 region of dorsal hippocampus reversed the increase in microglia number and size in dorsal CA1 and rescued memory deficits. These results indicate that CCR5 plays an important role in modulating Aß1-42-induced learning and memory deficits, and suggest that CCR5 antagonists may serve as a potential treatment to improve cognitive deficits associated with AD.

Microglial immunometabolism endophenotypes contribute to sex difference in Alzheimer's disease.

INTRODUCTION: The molecular mechanisms that contribute to sex differences, in particular female predominance, in Alzheimer's disease (AD) prevalence, symptomology, and pathology, are incompletely understood. METHODS: To address this problem, we investigated cellular metabolism and immune responses ("immunometabolism endophenotype") across AD individuals as a function of sex with diverse clinical diagnosis of cognitive status at death (cogdx), Braak staging, and Consortium to Establish a Registry for AD (CERAD) scores using human cortex metabolomics and transcriptomics data from the Religious Orders Study / Memory and Aging Project (ROSMAP) cohort. RESULTS: We identified sex-specific metabolites, immune and metabolic genes, and pathways associated with the AD diagnosis and progression. We identified female-specific elevation in glycerophosphorylcholine and N-acetylglutamate, which are AD inflammatory metabolites involved in interleukin (IL)-17 signaling, C-type lectin receptor, interferon signaling, and Toll-like receptor pathways. We pinpointed distinct microglia-specific immunometabolism endophenotypes (i.e., lipid- and amino acid-specific IL-10 and IL-17 signaling pathways) between female and male AD subjects. In addition, female AD subjects showed evidence of diminished excitatory neuron and microglia communications via glutamate-mediated immunometabolism. DISCUSSION: Our results point to new understanding of the molecular basis for female predominance in AD, and warrant future independent validations with ethnically diverse patient cohorts to establish a likely causal relationship of microglial immunometabolism in the sex differences in AD. HIGHLIGHTS: Sex-specific immune metabolites, gene networks and pathways, are associated with Alzheimer's disease pathogenesis and disease progression. Female AD subjects exhibit microglial immunometabolism endophenotypes characterized by decreased glutamate metabolism and elevated interleukin-10 pathway activity. Female AD subjects showed a shift in glutamate-mediated cell-cell communications between excitatory neurons to microglia and astrocyte.

Dual-Affinity Graphene Sheets for High-Resolution Cryo-Electron Microscopy.

With the development of cryo-electron microscopy (cryo-EM), high-resolution structures of macromolecules can be reconstructed by the single particle method efficiently. However, challenges may still persist during the specimen preparation stage. Specifically, proteins tend to adsorb at the air-water interface and exhibit a preferred orientation in vitreous ice. To overcome these challenges, we have explored dual-affinity graphene (DAG) modified with two different affinity ligands as a supporting material for cryo-EM sample preparation. The ligands can bind to distinct sites on the corresponding tagged particles, which in turn generates various orientation distributions of particles and prevents the adsorption of protein particles onto the air-water interface. As expected, the DAG exhibited high binding specificity and affinity to target macromolecules, resulting in more balanced particle Euler angular distributions compared to single functionalized graphene on two different protein cases, including the SARS -CoV-2 spike glycoprotein. We anticipate that the DAG grids will enable facile and efficient three-dimensional (3D) reconstruction for cryo-EM structural determination, providing a robust and general technique for future studies.

Domino-like stacking order switching in twisted monolayer-multilayer graphene.

Atomic reconstruction has been widely observed in two-dimensional van der Waals structures with small twist angles1-7. This unusual behaviour leads to many novel phenomena, including strong electronic correlation, spontaneous ferromagnetism and topologically protected states1,5,8-14. Nevertheless, atomic reconstruction typically occurs spontaneously, exhibiting only one single stable state. Using conductive atomic force microscopy, here we show that, for small-angle twisted monolayer-multilayer graphene, there exist two metastable reconstruction states with distinct stacking orders and strain soliton structures. More importantly, we demonstrate that these two reconstruction states can be reversibly switched, and the switching can propagate spontaneously in an unusual domino-like fashion. Assisted by lattice-resolved conductive atomic force microscopy imaging and atomistic simulations, the detailed structure of the strain soliton networks has been identified and the associated propagation mechanism is attributed to the strong mechanical coupling among solitons. The fine structure of the bistable states is critical for understanding the unique properties of van der Waals structures with tiny twists, and the switching mechanism offers a viable means for manipulating their stacking states.

A network medicine approach to investigation and population-based validation of disease manifestations and drug repurposing for COVID-19.

The global coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to unprecedented social and economic consequences. The risk of morbidity and mortality due to COVID-19 increases dramatically in the presence of coexisting medical conditions, while the underlying mechanisms remain unclear. Furthermore, there are no approved therapies for COVID-19. This study aims to identify SARS-CoV-2 pathogenesis, disease manifestations, and COVID-19 therapies using network medicine methodologies along with clinical and multi-omics observations. We incorporate SARS-CoV-2 virus-host protein-protein interactions, transcriptomics, and proteomics into the human interactome. Network proximity measurement revealed underlying pathogenesis for broad COVID-19-associated disease manifestations. Analyses of single-cell RNA sequencing data show that co-expression of ACE2 and TMPRSS2 is elevated in absorptive enterocytes from the inflamed ileal tissues of Crohn disease patients compared to uninflamed tissues, revealing shared pathobiology between COVID-19 and inflammatory bowel disease. Integrative analyses of metabolomics and transcriptomics (bulk and single-cell) data from asthma patients indicate that COVID-19 shares an intermediate inflammatory molecular profile with asthma (including IRAK3 and ADRB2). To prioritize potential treatments, we combined network-based prediction and a propensity score (PS) matching observational study of 26,779 individuals from a COVID-19 registry. We identified that melatonin usage (odds ratio [OR] = 0.72, 95% CI 0.56-0.91) is significantly associated with a 28% reduced likelihood of a positive laboratory test result for SARS-CoV-2 confirmed by reverse transcription-polymerase chain reaction assay. Using a PS matching user active comparator design, we determined that melatonin usage was associated with a reduced likelihood of SARS-CoV-2 positive test result compared to use of angiotensin II receptor blockers (OR = 0.70, 95% CI 0.54-0.92) or angiotensin-converting enzyme inhibitors (OR = 0.69, 95% CI 0.52-0.90). Importantly, melatonin usage (OR = 0.48, 95% CI 0.31-0.75) is associated with a 52% reduced likelihood of a positive laboratory test result for SARS-CoV-2 in African Americans after adjusting for age, sex, race, smoking history, and various disease comorbidities using PS matching. In summary, this study presents an integrative network medicine platform for predicting disease manifestations associated with COVID-19 and identifying melatonin for potential prevention and treatment of COVID-19.

Performance of heart rate adjusted heart rate variability for risk stratification of sudden cardiac death.

PURPOSE: As a non-invasive tool for the assessment of cardiovascular autonomic function, the predictive value of heart rate variability (HRV) for sudden cardiac death (SCD) risk stratification remains unclear. In this study, we investigated the performance of the individualized heart rate (HR) adjusted HRV (HRVI) for SCD risk stratification in subjects with diverse risks. METHODS: A total of 11 commonly used HRV metrics were analyzed in 192 subjects, including 88 healthy controls (low risk group), 82 hypertrophic cardiomyopathy (HCM) patients (medium risk group), and 22 SCD victims (high risk group). The relationship between HRV metrics and HR was examined with long-term and short-term analysis. The performance HRVI was evaluated by area under the receiver operating characteristic curve (AUC) and covariance of variation (CV). RESULTS: Most of the HRV metrics were exponentially decayed with the increase of HR, while the exponential power coefficients were significantly different among groups. The HRVI metrics discriminated low, medium and high risk subjects with a median AUC of 0.72[0.11], which was considerably higher than that of the traditional long-term (0.63[0.04]) and short-term (0.58[0.05]) HRV without adjustment. The average CV of the HRVI metrics was also significantly lower than traditional short-term HRV metrics (0.09 ± 0.02 vs. 0.24 ± 0.13, p < 0.01). CONCLUSIONS: Subjects with diverse risks of SCD had similar exponential decay relationship between HRV metrics and HR, but with different decaying rates. HRVI provides reliable and robust estimation for risk stratification of SCD.

Genome-wide DNA methylation analysis of Astragalus and Danshen on the intervention of myofibroblast activation in idiopathic pulmonary fibrosis.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF), a chronic progressive interstitial lung disease of unknown etiology, is characterized by continuous damage to alveolar epithelial cells, abnormal repair of alveolar tissue, and alveolar wall scar formation. Currently, the recommended treatment for IPF in Western medicine is relatively limited. In contrast, traditional Chinese medicine and compound prescriptions show advantages in the diagnosis and treatment of IPF, which can be attributed to their multi-channel and multi-target characteristics and minimal side-effects. The purpose of this study was to further corroborate the effectiveness and significance of the traditional Chinese medications Astragalus and Danshen in IPF treatment. METHODS: We performed whole-genome methylation analysis on nine rat lung tissue samples to determine the epigenetic variation between IPF and non-fibrotic lungs using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses and quantitative reverse transcription polymerase chain reactions. RESULTS: We identified differentially methylated regions and 105 associated key functional genes in samples related to IPF and Chinese medicine treatment. Based on the methylation levels and gene expression profiles between the Chinese medicine intervention and pulmonary fibrosis model groups, we speculated that Astragalus and Salvia miltiorrhiza (traditionally known as Danshen) act on the Isl1, forkhead box O3, and Sonic hedgehog genes via regulation at transcriptional and epigenetic levels during IPF. CONCLUSIONS: These findings provide novel insights into the epigenetic regulation of IPF, indicate the effectiveness of Astragalus and Danshen in treating IPF, and suggest several promising therapeutic targets for preventing and treating IPF.

Population-based discovery and Mendelian randomization analysis identify telmisartan as a candidate medicine for Alzheimer's disease in African Americans.

INTRODUCTION: African Americans (AAs) and European Americans (EAs) differ in Alzheimer's disease (AD) prevalence, risk factors, and symptomatic presentation and AAs are less likely to enroll in AD clinical trials. METHODS: We conducted race-conscious pharmacoepidemiologic studies of 5.62 million older individuals (age ≥60) to investigate the association of telmisartan exposure and AD outcome using Cox analysis, Kaplan-Meier analysis, and log-rank test. We performed Mendelian randomization (MR) analysis of large ethnically diverse genetic data to test likely causal relationships between telmisartan's target and AD. RESULTS: We identified that moderate/high telmisartan exposure was significantly associated with a reduced incidence of AD in the AAs compared to low/no telmisartan exposure (hazard ratio [HR] = 0.77, 95% CI: 0.65-0.91, p-value = 0.0022), but not in the non-Hispanic EAs (HR = 0.97, 95% CI: 0.89-1.05, p-value = 0.4110). Sensitivity and sex-/age-stratified patient subgroup analyses identified that telmisartan's medication possession ratio (MPR) and average hypertension daily dosage were significantly associated with a stronger reduction in the incidence of both AD and dementia in AAs. Using MR analysis from large genome-wide association studies (GWAS) (over 2 million individuals) across AD, hypertension, and diabetes, we further identified AA-specific beneficial effects of telmisartan for AD. DISCUSSION: Randomized controlled trials with ethnically diverse patient cohorts are warranted to establish causality and therapeutic outcomes of telmisartan and AD. HIGHLIGHTS: Telmisartan is associated with lower risk of Alzheimer's disease (AD) in African Americans (AAs). Telmisartan is the only angiotensin II receptor blockers having PPAR-γ agonistic properties with beneficial anti-diabetic and renal function effects, which mitigate AD risk in AAs. Mendelian randomization (MR) analysis demonstrates the specificity of telmisartan's protective mechanism to AAs.

Polarization-controlled single-particle scattering imaging spectroscopy using waveguide excitation.

An imaging spectroscopic system that enables spatially-resolved detection of single-particle scattering with polarization-controlled waveguide excitation scheme is presented. The detected microscopic images of inhomogeneous nanostructures are recorded in a time sequence into a data cube based on a Michelson interferometer. The interferograms on selected pixels are Fourier-transformed into multiple spectra. The waveguide excitation scheme is presented for both transmission and reflection measurements while the dark-field excitation scheme is presented in transmission measurements for comparison. Gold nanoparticles, nanorods, and particles on film are utilized in the detection of polarization-dependent spectra. Measurement results are verified with the finite-difference time-domain (FDTD) simulations. The polarization-controlled coupling conditions in nanorods and particle-on-film systems are discussed with simulated field distributions around the nanostructures.

Okicamelliaside targets the N-terminal chaperone pocket of HSP90 disrupts the chaperone protein interaction of HSP90-CDC37 and exerts antitumor activity.

Heat shock protein 90 (HSP90) has been recognized as a crucial target in cancer cells. However, various toxic reactions targeting the ATP binding site of HSP90 may not be the best choice for HSP90 inhibitors. In this paper, an ellagic acid derivative, namely, okicamelliaside (OCS), with antitumor effects was found. To identify potential anti-cancer mechanisms, an OCS photosensitive probe was applied to target fishing and tracing. Chemical proteomics and protein-drug interaction experiments have shown that HSP90 is a key target for OCS, with a strong binding affinity (KD = 6.45 µM). Mutation analysis of the target protein and molecular dynamics simulation revealed that OCS could competitively act on the key Glu-47 site at the N-terminal chaperone pocket of HSP90, where the co-chaperone CDC37 binds to HSP90, affect its stability and reduce the ∆Gbind of HSP90-CDC37. It was demonstrated that OCS destroys the protein-protein interactions of HSP90-CDC37; selectively affects downstream kinase client proteins of HSP90, including CDK4, P-AKT473, and P-ERK1/2; and exerts antitumor effects on A549 cells. Furthermore, tumor xenograft experiments demonstrated high antitumor activity and low toxicity of OCS in the same way. Our findings identified a novel N-terminal chaperone pocket natural inhibitor of HSP90, that is, OCS, which selectively inhibits the formation of the HSP90-CDC37 protein complex, and provided further insight into HSP90 inhibitors for anti-cancer candidate drugs.

The responses of activated sludge to membrane cleaning reagent H2O2 and protection of extracellular polymeric substances.

Hydrogen peroxide (H2O2) is evaluated as a potential replacement for chlorine to control biofouling in membrane bioreactors (MBRs). However, H2O2 might diffuse into the mixed liquor and damage microorganisms during membrane cleaning. This study comprehensively analyzed the impacts of H2O2 on microbes. Key enzymes involved in phenol biodegradation were inhibited with H2O2 concentration increased, and thus phenol degradation efficiency was decreased. Increase of lactic dehydrogenase (LDH) and intracellular reactive oxygen species (ROS) indicated more severe cell rupture with H2O2 concentration increased. At the same H2O2 concentration, Extracellular polymeric substances (EPS) extraction further led to inhibiting the activity of key enzymes, decreasing phenol degradation efficiency, and enhancing LDH release and ROS production, demonstrating that the existence of EPS moderated the adverse impacts on microbes. Spectroscopic characterization revealed the increase of H2O2 decreased tryptophan protein-like substances, protein-associated bonds and polysaccharide-associated bonds. Hydroxyl and amide groups in EPS were attacked, which might lead to the consumption of H2O2, indicated EPS protect the microorganism through sacrificial reaction with H2O2.

Tuning friction to a superlubric state via in-plane straining.

Controlling, and in many cases minimizing, friction is a goal that has long been pursued in history. From the classic Amontons-Coulomb law to the recent nanoscale experiments, the steady-state friction is found to be an inherent property of a sliding interface, which typically cannot be altered on demand. In this work, we show that the friction on a graphene sheet can be tuned reversibly by simple mechanical straining. In particular, by applying a tensile strain (up to 0.60%), we are able to achieve a superlubric state (coefficient of friction nearly 0.001) on a suspended graphene. Our atomistic simulations together with atomically resolved friction images reveal that the in-plane strain effectively modulates the flexibility of graphene. Consequently, the local pinning capability of the contact interface is changed, resulting in the unusual strain-dependent frictional behavior. This work demonstrates that the deformability of atomic-scale structures can provide an additional channel of regulating the friction of contact interfaces involving configurationally flexible materials.

Cardiac risk stratification in cancer patients: A longitudinal patient-patient network analysis.

BACKGROUND: Cardiovascular disease is a leading cause of death in general population and the second leading cause of mortality and morbidity in cancer survivors after recurrent malignancy in the United States. The growing awareness of cancer therapy-related cardiac dysfunction (CTRCD) has led to an emerging field of cardio-oncology; yet, there is limited knowledge on how to predict which patients will experience adverse cardiac outcomes. We aimed to perform unbiased cardiac risk stratification for cancer patients using our large-scale, institutional electronic medical records. METHODS AND FINDINGS: We built a large longitudinal (up to 22 years' follow-up from March 1997 to January 2019) cardio-oncology cohort having 4,632 cancer patients in Cleveland Clinic with 5 diagnosed cardiac outcomes: atrial fibrillation, coronary artery disease, heart failure, myocardial infarction, and stroke. The entire population includes 84% white Americans and 11% black Americans, and 59% females versus 41% males, with median age of 63 (interquartile range [IQR]: 54 to 71) years old. We utilized a topology-based K-means clustering approach for unbiased patient-patient network analyses of data from general demographics, echocardiogram (over 25,000), lab testing, and cardiac factors (cardiac). We performed hazard ratio (HR) and Kaplan-Meier analyses to identify clinically actionable variables. All confounding factors were adjusted by Cox regression models. We performed random-split and time-split training-test validation for our model. We identified 4 clinically relevant subgroups that are significantly correlated with incidence of cardiac outcomes and mortality. Among the 4 subgroups, subgroup I (n = 625) has the highest risk of de novo CTRCD (28%) with an HR of 3.05 (95% confidence interval (CI) 2.51 to 3.72). Patients in subgroup IV (n = 1,250) had the worst survival probability (HR 4.32, 95% CI 3.82 to 4.88). From longitudinal patient-patient network analyses, the patients in subgroup I had a higher percentage of de novo CTRCD and a worse mortality within 5 years after the initiation of cancer therapies compared to long-time exposure (6 to 20 years). Using clinical variable network analyses, we identified that serum levels of NT-proB-type Natriuretic Peptide (NT-proBNP) and Troponin T are significantly correlated with patient's mortality (NT-proBNP > 900 pg/mL versus NT-proBNP = 0 to 125 pg/mL, HR = 2.95, 95% CI 2.28 to 3.82, p < 0.001; Troponin T > 0.05 µg/L versus Troponin T ≤ 0.01 µg/L, HR = 2.08, 95% CI 1.83 to 2.34, p < 0.001). Study limitations include lack of independent cardio-oncology cohorts from different healthcare systems to evaluate the generalizability of the models. Meanwhile, the confounding factors, such as multiple medication usages, may influence the findings. CONCLUSIONS: In this study, we demonstrated that the patient-patient network clustering methodology is clinically intuitive, and it allows more rapid identification of cancer survivors that are at greater risk of cardiac dysfunction. We believed that this study holds great promise for identifying novel cardiac risk subgroups and clinically actionable variables for the development of precision cardio-oncology.

Network-based prediction of drug-target interactions using an arbitrary-order proximity embedded deep forest.

MOTIVATION: Systematic identification of molecular targets among known drugs plays an essential role in drug repurposing and understanding of their unexpected side effects. Computational approaches for prediction of drug-target interactions (DTIs) are highly desired in comparison to traditional experimental assays. Furthermore, recent advances of multiomics technologies and systems biology approaches have generated large-scale heterogeneous, biological networks, which offer unexpected opportunities for network-based identification of new molecular targets among known drugs. RESULTS: In this study, we present a network-based computational framework, termed AOPEDF, an arbitrary-order proximity embedded deep forest approach, for prediction of DTIs. AOPEDF learns a low-dimensional vector representation of features that preserve arbitrary-order proximity from a highly integrated, heterogeneous biological network connecting drugs, targets (proteins) and diseases. In total, we construct a heterogeneous network by uniquely integrating 15 networks covering chemical, genomic, phenotypic and network profiles among drugs, proteins/targets and diseases. Then, we build a cascade deep forest classifier to infer new DTIs. Via systematic performance evaluation, AOPEDF achieves high accuracy in identifying molecular targets among known drugs on two external validation sets collected from DrugCentral [area under the receiver operating characteristic curve (AUROC) = 0.868] and ChEMBL (AUROC = 0.768) databases, outperforming several state-of-the-art methods. In a case study, we showcase that multiple molecular targets predicted by AOPEDF are associated with mechanism-of-action of substance abuse disorder for several marketed drugs (such as aripiprazole, risperidone and haloperidol). AVAILABILITY AND IMPLEMENTATION: Source code and data can be downloaded from https://github.com/ChengF-Lab/AOPEDF.